Overview of Contrast Data Mining as a Field and Preview of an Upcoming Book
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paper_title: On the relation between jumping emerging patterns and rough set theory with application to data classification

paper_content:

Contrast patterns are an essential element of classification methods based on data mining. Among many propositions, jumping emerging patterns (JEPs) have gained significant recognition due to their simplicity and strong discrimination capabilities. This thesis considers JEPs in terms of discovery and classification. The focus is put on their correspondence to the rough set theory. Transformations between transactional data and decision tables allow us to demonstrate relations of JEPs and global/local reducts. As a part of this discussion, we introduce the concept of a jumping emerging pattern with negation (JEPN). Our observations lead to two novel JEP mining methods based on local reducts: global condensation and local projection. Both attempt to decrease dimensionality of subproblems prior to reduct computation. We show that JEP mining can be reduced to the reduct set problem. The latter is addressed with a new approach, called RedApriori, that follows an Apriori candidate generation scheme and employs pruning based on the notion of attribute set dependence. In addition, we discuss different ways of storing pattern collections and propose a CC-Trie, a tree structure that ensures compactness of information and fast pattern lookups. ::: ::: A classic mining method for highly-supported JEPs employs a structure called a CP-Tree. We show how attribute set dependence can be employed in this approach to extend the pruning capabilities. Moreover, the problem of finding top-k most supported minimal JEPs is proposed. We discuss a solution that gradually raises minimal support while a CPTree is being mined. Small training sets are a challenge in classification. To improve accuracy, we propose AdaAccept, an adaptive classification meta-scheme that analyzes testing instances in turns. It employs an internal classifier with reject option that modifies itself only with accepted instances. Furthermore, we consider a concretization of this scheme in the field of emerging patterns, AdaptiveJEP-Classifier. Two adaptation methods, support adjustment and border recomputation, are put forward. The work has both theoretical and experimental character. The proposed methods and optimizations are evaluated and compared against solutions known in the literature.
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paper_title: Mining Class Contrast Functions by Gene Expression Programming

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Finding functions whose accuracies change significantly between two classes is an interesting work. In this paper, this kind of functions is defined as class contrast functions. As Gene Expression Programming (GEP) can discover essential relations from data and express them mathematically, it is desirable to apply GEP to mining such class contrast functions from data. The main contributions of this paper include: (1) proposing a new data mining task --- class contrast function mining, (2) designing a GEP based method to find class contrast functions, (3) presenting several strategies for finding multiple class contrast functions in data, (4) giving an extensive performance study on both synthetic and real world datasets. The experimental results show that the proposed methods are effective. Several class contrast functions are discovered from the real world datasets. Some potential works on class contrast function mining are discussed based on the experimental results.
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paper_title: Mining contrast inequalities in numeric dataset

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Finding relational expressions which exist frequently in one class of data while not in the other class of data is an interesting work. In this paper, a relational expression of this kind is defined as a contrast inequality. Gene Expression Programming (GEP) is powerful to discover relations from data and express them in mathematical level. Hence, it is desirable to apply GEP to such mining task. The main contributions of this paper include: (1) introducing the concept of contrast inequality mining, (2) designing a two-genome chromosome structure to guarantee that each individual in GEP is a valid inequality, (3) proposing a new genetic mutation to improve the efficiency of evolving contrast inequalities, (4) presenting a GEP-based method to discover contrast inequalities, (5) giving an extensive performance study on real-world datasets. The experimental results show that the proposed methods are effective. Contrast inequalities with high discriminative power are discovered from the real-world datasets. Some potential works on contrast inequality mining are discussed.
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paper_title: Extracting semantics in OLAP databases using emerging cubes

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Data cubes capture general trends aggregated from multidimensional data from a categorical relation. When provided with two relations, interesting knowledge can be exhibited by comparing the two underlying data cubes. Trend reversals or particular phenomena irrelevant in one data cube may indeed clearly appear in the other data cube. In order to capture such trend reversals, we have proposed the concept of Emerging Cube. In this article, we emphasize on two new approaches for computing Emerging Cubes. Both are devised to be integrated within standard Olap systems, since they do not require any additional nor complex data structures. Our rst approach is based on Sql. We propose three queries with dierent aims. The most ecient query uses a particular data structure merging the two input relations to achieve a single data cube computation. This query works ne even when voluminous data are processed. Our second approach is algorithmic and aims to improve eciency and scalability while preserving integration capability. The E-Idea algorithm works a la Buc and takes the specic features of Emerging Cubes into account. EIdea is automaton-based and adapts its behavior to the current execution context. Our proposals are validated by various experiments where we measure query response time. Comparative experiments show that E-Idea’s response time is proportional to the size of the Emerging Cube. Experiments also demonstrate that extracting Emerging Cubes can be computed in practice, in a time compatible with user expectations.
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paper_title: Fast mining of high dimensional expressive contrast patterns using zero-suppressed binary decision diagrams

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Patterns of contrast are a very important way of comparing multi-dimensional datasets. Such patterns are able to capture regions of high difference between two classes of data, and are useful for human experts and the construction of classifiers. However, mining such patterns is particularly challenging when the number of dimensions is large. This paper describes a new technique for mining several varieties of contrast pattern, based on the use of Zero-Suppressed Binary Decision Diagrams (ZBDDs), a powerful data structure for manipulating sparse data. We study the mining of both simple contrast patterns, such as emerging patterns, and more novel and complex contrasts, which we call disjunctive emerging patterns. A performance study demonstrates our ZBDD technique is highly scalable, substantially improves on state of the art mining for emerging patterns and can be effective for discovering complex contrasts from datasets with thousands of attributes.
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paper_title: Mining statistically important equivalence classes and delta-discriminative emerging patterns

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The support-confidence framework is the most common measure used in itemset mining algorithms, for its antimonotonicity that effectively simplifies the search lattice. This computational convenience brings both quality and statistical flaws to the results as observed by many previous studies. In this paper, we introduce a novel algorithm that produces itemsets with ranked statistical merits under sophisticated test statistics such as chi-square, risk ratio, odds ratio, etc. Our algorithm is based on the concept of equivalence classes. An equivalence class is a set of frequent itemsets that always occur together in the same set of transactions. Therefore, itemsets within an equivalence class all share the same level of statistical significance regardless of the variety of test statistics. As an equivalence class can be uniquely determined and concisely represented by a closed pattern and a set of generators, we just mine closed patterns and generators, taking a simultaneous depth-first search scheme. This parallel approach has not been exploited by any prior work. We evaluate our algorithm on two aspects. In general, we compare to LCM and FPclose which are the best algorithms tailored for mining only closed patterns. In particular, we compare to epMiner which is the most recent algorithm for mining a type of relative risk patterns, known as minimal emerging patterns. Experimental results show that our algorithm is faster than all of them, sometimes even multiple orders of magnitude faster. These statistically ranked patterns and the efficiency have a high potential for real-life applications, especially in biomedical and financial fields where classical test statistics are of dominant interest.
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paper_title: Fast Algorithms for Mining Emerging Patterns

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Emerging Patterns are itemsets whose supports change significantly from one dataset to another. They are useful as a means of discovering distinctions inherently present amongst a collection of datasets and have been shown to be a powerful technique for constructing accurate classifiers. The task of finding such patterns is challenging though, and efficient techniques for their mining are needed.In this paper, we present a new mining method for a particular type of emerging pattern known as a jumping emerging pattern. The basis of our algorithm is the construction of trees, whose structure specifically targets the likely distribution of emerging patterns. The mining performance is typically around 5 times faster than earlier approaches. We then examine the problem of computing a useful subset of the possible emerging patterns. We show that such patterns can be mined even more efficiently (typically around 10 times faster), with little loss of precision.
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paper_title: Discovery of highly differentiative gene groups from microarray gene expression data using the gene club approach.

paper_content:

MOTIVATION ::: It is commonly believed that suitable analysis of microarray gene expression profile data can lead to better understanding of diseases, and better ways to diagnose and treat diseases. To achieve those goals, it is of interest to discover the gene interaction networks, and perhaps even pathways, underlying given diseases from such data. In this paper, we consider methods for efficiently discovering highly differentiative gene groups (HDGG), which may provide insights on gene interaction networks. HDGGs are groups of genes which completely or nearly completely characterize the diseased or normal tissues. Discovering HDGGs is challenging, due to the high dimensionality of the data. ::: ::: ::: RESULTS ::: Our methods are based on the novel concept of gene clubs. A gene club consists of a set of genes having high potential to be interactive with each other. The methods can (i) efficiently discover signature HDGGs which completely characterize the diseased and the normal tissues respectively, (ii) find strongest or near strongest HDGGs containing any given gene, and (iii) find much stronger HDGGs than previous methods. As part of the experimental evaluation, the methods are applied to colon, prostate, ovarian, and breast cancer, and leukemia and so on. Some of the genes in the extracted signature HDGGs have known biological functions, and some have attracted little attention in biology and medicine. We hope that appropriate study on them can lead to medical breakthroughs. Some HDGGs for colon and prostate cancers are listed here. The website listed below contains HDGGs for the other cancers. ::: ::: ::: AVAILABILITY ::: HDGG is implemented in C++ and runs on Unix or Windows platform. The code is available at: http://www.cs.wright.edu/~gdong/hdgg/.
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paper_title: Fast mining of high dimensional expressive contrast patterns using zero-suppressed binary decision diagrams

paper_content:

Patterns of contrast are a very important way of comparing multi-dimensional datasets. Such patterns are able to capture regions of high difference between two classes of data, and are useful for human experts and the construction of classifiers. However, mining such patterns is particularly challenging when the number of dimensions is large. This paper describes a new technique for mining several varieties of contrast pattern, based on the use of Zero-Suppressed Binary Decision Diagrams (ZBDDs), a powerful data structure for manipulating sparse data. We study the mining of both simple contrast patterns, such as emerging patterns, and more novel and complex contrasts, which we call disjunctive emerging patterns. A performance study demonstrates our ZBDD technique is highly scalable, substantially improves on state of the art mining for emerging patterns and can be effective for discovering complex contrasts from datasets with thousands of attributes.
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paper_title: Making Use of the Most Expressive Jumping Emerging Patterns for Classification

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Abstract. Classification aims to discover a model from training data that can be used to predict the class of test instances. In this paper, we propose the use of jumping emerging patterns (JEPs) as the basis for a new classifier called the JEP-Classifier. Each JEP can capture some crucial difference between a pair of datasets. Then, aggregating all JEPs of large supports can produce a more potent classification power. Procedurally, the JEP-Classifier learns the pair-wise features (sets of JEPs) contained in the training data, and uses the collective impacts contributed by the most expressive pair-wise features to determine the class labels of the test data. Using only the most expressive JEPs in the JEP-Classifier strengthens its resistance to noise in the training data, and reduces its complexity (as there are usually a very large number of JEPs). We use two algorithms for constructing the JEP-Classifier which are both scalable and efficient. These algorithms make use of the border representation to efficiently store and manipulate JEPs. We also present experimental results which show that the JEP-Classifier achieves much higher testing accuracies than the association-based classifier of (Liu et al, 1998), which was reported to outperform C4.5 in general.
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paper_title: CPAR: Classification based on Predictive Association Rules

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Recent studies in data mining have proposed a new classification approach, called associative classification, which, according to several reports, such as [7, 6], achieves higher classification accuracy than traditional classification approaches such as C4.5. However, the approach also suffers from two major deficiencies: (1) it generates a very large number of association rules, which leads to high processing overhead; and (2) its confidence-based rule evaluation measure may lead to overfitting. In comparison with associative classification, traditional rule-based classifiers, such as C4.5, FOIL and RIPPER, are substantially faster but their accuracy, in most cases, may not be as high. In this paper, we propose a new classification approach, CPAR (Classification based on Predictive Association Rules), which combines the advantages of both associative classification and traditional rule-based classification. Instead of generating a large number of candidate rules as in associative classification, CPAR adopts a greedy algorithm to generate rules directly from training data. Moreover, CPAR generates and tests more rules than traditional rule-based classifiers to avoid missing important rules. To avoid overfitting, CPAR uses expected accuracy to evaluate each rule and uses the best k rules in prediction.
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paper_title: Masquerader Detection Using OCLEP: One-Class Classification Using Length Statistics of Emerging Patterns

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We introduce a new method for masquerader detection that only uses a user?s own data for training, called Oneclass Classification using Length statistics of Emerging Patterns (OCLEP). Emerging patterns (EPs) are patterns whose support increases from one dataset/class to another with a big ratio, and have been very useful in earlier studies. OCLEP classifies a case T as self or masquerader by using the average length of EPs obtained by contrasting T against sets of samples of a user?s normal data. It is based on the observation that one needs long EPs to differentiate instances from a common class, but needs short EPs to differentiate instances from different classes. OCLEP has two novel features: for training it uses EPs mined from just the self class; for classification it uses the length statistics instead of the EPs themselves. Experiments show that OCLEP can achieve very good accuracy while keeping the false positive rate low, it achieves slightly better area-under-ROC-curve than SVM, and it can achieve good results when other approaches can not. OCLEP requires little effort in choosing parameters; the SVM requires significant tuning and it is hard to reach the theoretical optimal result. These features imply that OCLEP is a good complementary component for a robust masquerader detection system, even though its average performance in false positive rate is not as good as SVM?s.
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paper_title: A weighting scheme based on emerging patterns for weighted support vector machines

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Support vector machines (SVMs) are powerful tools for solving classification problems and have been applied to many application fields, such as pattern recognition and data mining, in the past decade. Weighted support vector machines (weighted SVMs) extend SVMs by considering that different input vectors make different contributions to the learning of decision surface. An important issue in training weighted SVMs is how to develop a reliable weighting model to reflect the true noise distribution in the training data, i.e., noise and outliers should have low weights. In this paper, we propose to use emerging patterns (EPs) to construct such a model. EPs are those itemsets whose supports in one class are significantly higher than their supports in the other class. Since EPs of a given class represent the discriminating knowledge unique to their home class, noise and outliers should contain no EPs or EPs of the both contradicting classes, while a representative instance of the class should contain strong EPs of the same class. We calculate numeric scores for each instance based on EPs, and then assign weights to the training data using those scores. An extensive experiment carried out on a large number of benchmark datasets show that our weighting scheme often improves the performance of weighted SVMs over SVMs. We argue that the improvement is due to the ability of our model to approximate the true distribution of data points.
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paper_title: Integrating classification and association rule mining

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Classification rule mining aims to discover a small set of rules in the database that forms an accurate classifier. Association rule mining finds all the rules existing in the database that satisfy some minimum support and minimum confidence constraints. For association rule mining, the target of discovery is not pre-determined, while for classification rule mining there is one and only one predetermined target. In this paper, we propose to integrate these two mining techniques. The integration is done by focusing on mining a special subset of association rules, called class association rules (CARs). An efficient algorithm is also given for building a classifier based on the set of discovered CARs. Experimental results show that the classifier built this way is, in general, more accurate than that produced by the state-of-the-art classification system C4.5. In addition, this integration helps to solve a number of problems that exist in the current classification systems.
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paper_title: Information-Based Classification by Aggregating Emerging Patterns

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Emerging patterns (EPs) are knowledge patterns capturing contrasts between data classes. In this paper, we propose an information-based approach for classification by aggregating emerging patterns. The constraint-based EP mining algorithm enables the system to learn from large-volume and high-dimensional data; the new approach for selecting representative EPs and efficient algorithm for finding the EPs renders the system high predictive accuracy and short classification time. Experiments on many benchmark datasets show that the resulting classifiers have good overall predictive accuracy, and are often also superior to other state-of-the-art classification systems such as C4.5, CBA and LB.
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paper_title: DeEPs: A New Instance-Based Lazy Discovery and Classification System

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Distance is widely used in most lazy classification systems. Rather than using distance, we make use of the frequency of an instance's subsets of features and the frequency-change rate of the subsets among training classes to perform both knowledge discovery and classification. We name the system DeEPs. Whenever an instance is considered, DeEPs can efficiently discover those patterns contained in the instance which sharply differentiate the training classes from one to another. DeEPs can also predict a class label for the instance by compactly summarizing the frequencies of the discovered patterns based on a view to collectively maximize the discriminating power of the patterns. Many experimental results are used to evaluate the system, showing that the patterns are comprehensible and that DeEPs is accurate and scalable.
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paper_title: Using emerging patterns and decision trees in rare-class classification

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The problem of classifying rarely occurring cases is faced in many real life applications. The scarcity of the rare cases makes it difficult to classify them correctly using traditional classifiers. In this paper, we propose an approach to use emerging patterns (EPs) (G. Dong and J. Li, 1999) and decision trees (DTs) in rare-class classification (EPDT). EPs are those itemsets whose supports in one class are significantly higher than their supports in the other classes. EPDT employs the power of EPs to improve the quality of rare-case classification. To achieve this aim, we first introduce the idea of generating nonexisting rare-class instances, and then we over-sample the most important rare-class instances. Our experiments show that EPDT outperforms many classification methods.
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paper_title: Using emerging patterns to construct weighted decision trees

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Decision trees (DTs) represent one of the most important and popular solutions to the problem of classification. They have been shown to have excellent performance in the field of data mining and machine learning. However, the problem of DTs is that they are built using data instances assigned to crisp classes. In this paper, we generalize decision trees so that they can take into account weighted classes assigned to the training data instances. Moreover, we propose a novel method for discovering weights for the training instances. Our method is based on emerging patterns (EPs). EPs are those itemsets whose supports (probabilities) in one class are significantly higher than their supports (probabilities) in the other classes. Our experimental evaluation shows that the new proposed method has good performance and excellent noise tolerance.
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paper_title: Discovering Dynamic Logical Blog Communities Based on Their Distinct Interest Profiles

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This paper addresses the problem of identifying dynamic logical blog communities based on the distinct interests shared by blogs in the communities. This paper is motivated by the facts that the blog space is highly dynamic both in the participating bloggers and in the interests/issues of concern to them in their blogs, and that many organizations are interested in identifying the evolution/emergence of various blog communities and the current interests/issues of concern to the blogs of those communities. Using dynamic shared distinct interests to define logical blog communities allows us to better identify and track the issues of concern to the bloggers than using statically chosen keywords or statically defined blog communities. The paper discusses algorithms to solve the above problem, which attempt to maximize discriminativeness and diversity of the distinct interests of the blog communities. Experiments are reported to evaluate the performance of the algorithms and to demonstrate their utility. Keywords-distinct interest; clustering with description; weblog community discovery; community description; contrast pattern.
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paper_title: A Contrast Pattern Based Clustering Quality Index for Categorical Data

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Since clustering is unsupervised and highly explorative, clustering validation (i.e. assessing the quality of clustering solutions) has been an important and long standing research problem. Existing validity measures have significant shortcomings. This paper proposes a novel Contrast Pattern based Clustering Quality index (CPCQ) for categorical data, by utilizing the quality and diversity of the contrast patterns (CPs) which contrast the clusters in clusterings. High quality CPs can characterize clusters and discriminate them against each other. Experiments show that the CPCQ index (1) can recognize that expert-determined classes are the best clusters for many datasets from the UCI repository; (2) does not give inappropriate preference to larger number of clusters; (3) does not require a user to provide a distance function.
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paper_title: Discovering Dynamic Logical Blog Communities Based on Their Distinct Interest Profiles

paper_content:

This paper addresses the problem of identifying dynamic logical blog communities based on the distinct interests shared by blogs in the communities. This paper is motivated by the facts that the blog space is highly dynamic both in the participating bloggers and in the interests/issues of concern to them in their blogs, and that many organizations are interested in identifying the evolution/emergence of various blog communities and the current interests/issues of concern to the blogs of those communities. Using dynamic shared distinct interests to define logical blog communities allows us to better identify and track the issues of concern to the bloggers than using statically chosen keywords or statically defined blog communities. The paper discusses algorithms to solve the above problem, which attempt to maximize discriminativeness and diversity of the distinct interests of the blog communities. Experiments are reported to evaluate the performance of the algorithms and to demonstrate their utility. Keywords-distinct interest; clustering with description; weblog community discovery; community description; contrast pattern.
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paper_title: Strong Compound-Risk Factors: Efficient Discovery Through Emerging Patterns and Contrast Sets

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Odds ratio (OR), relative risk (RR) (risk ratio), and absolute risk reduction (ARR) (risk difference) are biostatistics measurements that are widely used for identifying significant risk factors in dichotomous groups of subjects. In the past, they have often been used to assess simple risk factors. In this paper, we introduce the concept of compound-risk factors to broaden the applicability of these statistical tests for assessing factor interplays. We observe that compound-risk factors with a high risk ratio or a big risk difference have an one-to-one correspondence to strong emerging patterns or strong contrast sets-two types of patterns that have been extensively studied in the data mining field. Such a relationship has been unknown to researchers in the past, and efficient algorithms for discovering strong compound-risk factors have been lacking. In this paper, we propose a theoretical framework and a new algorithm that unify the discovery of compound- risk factors that have a strong OR, risk ratio, or a risk difference. Our method guarantees that all patterns meeting a certain test threshold can be efficiently discovered. Our contribution thus represents the first of its kind in linking the risk ratios and ORs to pattern mining algorithms, making it possible to find compound- risk factors in large-scale data sets. In addition, we show that using compound-risk factors can improve classification accuracy in probabilistic learning algorithms on several disease data sets, because these compound-risk factors capture the interdependency between important data attributes.
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paper_title: Simple rules underlying gene expression profiles of more than six subtypes of acute lymphoblastic leukemia ( ALL ) patients

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Motivations and Results: For classifying gene expression profiles or other types of medical data, simple rules are preferable to non-linear distance or kernel functions. This is because rules may help us understand more about the application in addition to performing an accurate classification. In this paper, we discover novel rules that describe the gene expression profiles of more than six subtypes of acute lymphoblastic leukemia (ALL) patients. We also introduce a new classifier, named PCL, to make effective use of the rules. PCL is accurate and can handle multiple parallel classifications. We evaluate this method by classifying 327 heterogeneous ALL samples. Our test error rate is competitive to that of support vector machines, and it is 71% better than C4.5, 50% better than Naive Bayes, and 43% better than k-nearest neighbour. Experimental results on another independent data sets are also presented to show the strength of our method. Availability: Under http://sdmc.lit.org.sg/GEDatasets/, click on Supplementary Information.
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paper_title: Supervised descriptive rule discovery: A unifying survey of contrast set, emerging pattern and subgroup mining

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This paper gives a survey of contrast set mining (CSM), emerging pattern mining (EPM), and subgroup discovery (SD) in a unifying framework named supervised descriptive rule discovery. While all these research areas aim at discovering patterns in the form of rules induced from labeled data, they use different terminology and task definitions, claim to have different goals, claim to use different rule learning heuristics, and use different means for selecting subsets of induced patterns. This paper contributes a novel understanding of these subareas of data mining by presenting a unified terminology, by explaining the apparent differences between the learning tasks as variants of a unique supervised descriptive rule discovery task and by exploring the apparent differences between the approaches. It also shows that various rule learning heuristics used in CSM, EPM and SD algorithms all aim at optimizing a trade off between rule coverage and precision. The commonalities (and differences) between the approaches are showcased on a selection of best known variants of CSM, EPM and SD algorithms. The paper also provides a critical survey of existing supervised descriptive rule discovery visualization methods.
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paper_title: Identifying good diagnostic gene groups from gene expression profiles using the concept of emerging patterns

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MOTIVATIONS AND RESULTS ::: Gene groups that are significantly related to a disease can be detected by conducting a series of gene expression experiments. This work is aimed at discovering special types of gene groups that satisfy the following property. In each group, its member genes are found to be one-to-one contained in pre-determined intervals of gene expression level with a large frequency in one class of cells but are never found unanimously in these intervals in the other class of cells. We call these gene groups emerging patterns, to emphasize the patterns' frequency changes between two classes of cells. We use effective discretization and gene selection methods to obtain the most discriminatory genes. We also use efficient algorithms to derive the patterns from these genes. According to our studies on the ALL/AML dataset and the colon tumor dataset, some patterns, which consist of one or more genes, can reach a high frequency of 90%, or even 100%. In other words, they nearly or fully dominate one class of cells, even though they rarely occur in the other class. The discovered patterns are used to classify new cells with a higher accuracy than other reported methods. Based on these patterns, we also conjecture the possibility of a personalized treatment plan which converts colon tumor cells into normal cells by modulating the expression levels of a few genes.
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paper_title: Jumping emerging patterns with occurrence count in image classification

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In this paper we propose an application of jumping emerging patterns (JEPs) to the classification of images. We define of a new type of patterns, namely the jumping emerging patterns with occurrence count (occJEPs), which allow reasoning in transaction databases with recurrent items. Such data is a frequently used representation of images, for which classification is one of the most important data mining problems that needs to be solved accurately and efficiently. We provide a formal definition of the new type of patterns, an outline of an algorithm for finding occJEPs and a comparison with other rule- and pattern-based classifiers for a selection of sample images.
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paper_title: Evaluation of inter-laboratory and cross-platform concordance of DNA microarrays through discriminating genes and classifier transferability.

paper_content:

Microarray technology has great potential for improving our understanding of biological processes, medical conditions, and diseases. Often, microarray datasets are collected using different microarray platforms (provided by different companies) under different conditions in different laboratories. The cross-platform and cross-laboratory concordance of the microarray technology needs to be evaluated before it can be successfully and reliably applied in biological/clinical practice. New measures and techniques are proposed for comparing and evaluating the quality of microarray datasets generated from different platforms/laboratories. These measures and techniques are based on the following philosophy: the practical usefulness of the microarray technology may be confirmed if discriminating genes and classifiers, which are the focus of most, if not all, comparative investigations, discovered/trained from data collected in one lab/platform combination can be transferred to another lab/platform combination. The rationale is that the nondiscriminating genes might not be as strongly regulated as the discriminating genes, by the biological process of the tissue cells under study, and hence they may behave more randomly than the discriminating genes. Our experiment results, on microarray datasets generated from different platforms/laboratories using the reference mRNA samples in the Microarray Quality Control (MAQC) project, showed that DNA microarrays can produce highly repeatable data in a cross-platform cross-lab manner, when one focuses on the discriminating genes and classifiers. In our comparative study, we compare samples of one type against samples of another type; the methodology can be applied to situations where one compares one arbitrary class of data against another. Other findings include: (1) using three discriminating-gene/classifier-based methods to test the concordance between microarray datasets gave consistent results; (2) when noisy (nondiscriminating) genes were removed, the microarray datasets from different laboratories using common platform were found to be highly concordant, and the data generated using most of the commercial platforms studied here were also found to be concordant with each other; (3) several series of artificial datasets with known degree of difference were created, to establish a bridge between consistency rate and P-value, allowing us to estimate P-value if consistency rate between two datasets is known.
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paper_title: Spatial Emerging Patterns for Scene Classification

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In this paper we propose a novel method of scene classification, based on the idea of mining emerging patterns between classes of images, represented in a symbolic manner. We use the 9DLT (Direction Lower Triangular) representation of images, which allows to describe scenes with a limited number of symbols, while still capturing spatial relationships between objects visible on the images. We show an efficient method of mining the proposed Spatial Emerging Patterns and present results of synthetic image classification experiments.
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paper_title: Discovery of geospatial discriminating patterns from remote sensing datasets

paper_content:

Large amounts of remotely sensed data calls for data mining techniques to fully utilize their rich information content. In this paper, we study new means of discovery and summarization of knowledge contained in the spatial patterns of remote sensing datasets. Several geospatial feature variables are fused together, and the vector of their values at each spatial cell is considered as a transaction to be used in association analysis. The concept of emerging patterns is applied to ascertain the variables that exert dominant in∞uence on the distribution of a selected class variable. A new value-iteration method is introduced to optimally split the spatial domain of the selected variable into two classes. This division is used to calculate the set of patterns that are emerging with respect to the two classes; these patterns are the controlling factors|they are responsible for the spatial distribution of the class variable. A method for a concise summarization of controlling factors is introduced using a similarity measure that is custom-made for the type of patterns stemmed from remote sensing measurements. Using such a similarity measure, controlling factors are clustered providing brief description of difierent manners, in which the class variable is constrained by the explanatory variables. We evaluate our method in a real-world application pertaining to the density of vegetation within the continental United States. Examination of patterns related to the high vegetation cover provides a summary of data dependencies that helps to develop a better empirical model of the vegetation growth.
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paper_title: Characterizing Discriminative Patterns

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Discriminative patterns are association patterns that occur with disproportionate frequency in some classes versus others, and have been studied under names such as emerging patterns and contrast sets. Such patterns have demonstrated considerable value for classification and subgroup discovery, but a detailed understanding of the types of interactions among items in a discriminative pattern is lacking. To address this issue, we propose to categorize discriminative patterns according to four types of item interaction: (i) driver-passenger, (ii) coherent, (iii) independent additive and (iv) synergistic beyond independent additive. Either of the last three is of practical importance, with the latter two representing a gain in the discriminative power of a pattern over its subsets. Synergistic patterns are most restrictive, but perhaps the most interesting since they capture a cooperative effect. For domains such as genetic research, differentiating among these types of patterns is critical since each yields very different biological interpretations. For general domains, the characterization provides a novel view of the nature of the discriminative patterns in a dataset, which yields insights beyond those provided by current approaches that focus mostly on pattern-based classification and subgroup discovery. This paper presents a comprehensive discussion that defines these four pattern types and investigates their properties and their relationship to one another. In addition, these ideas are explored for a variety of datasets (ten UCI datasets, one gene expression dataset and two genetic-variation datasets). The results demonstrate the existence, characteristics and statistical significance of the different types of patterns. They also illustrate how pattern characterization can provide novel insights into discriminative pattern mining and the discriminative structure of different datasets.
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paper_title: Characterising the difference

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Characterising the differences between two databases is an often occurring problem in Data Mining. Detection of change over time is a prime example, comparing databases from two branches is another one. The key problem is to discover the patterns that describe the difference. Emerging patterns provide only a partial answer to this question. In previous work, we showed that the data distribution can be captured in a pattern-based model using compression [12]. Here, we extend this approach to define a generic dissimilarity measure on databases. Moreover, we show that this approach can identify those patterns that characterise the differences between two distributions. Experimental results show that our method provides a well-founded way to independently measure database dissimilarity that allows for thorough inspection of the actual differences. This illustrates the use of our approach in real world data mining.
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Title: Overview of Contrast Data Mining as a Field and Preview of an Upcoming Book
Section 1: HIGH LEVEL VIEW OF CONTRASTING
Description 1: Provide a general introduction and background to the concept of contrasting, including various applications and the historical evolution of contrasting techniques.

Section 2: DEFINITIONS AND TERMINOLOGY FOR CONTRAST PATTERNS
Description 2: Define key terms and concepts related to contrast patterns, including the various types and their applications across different data formats.

Section 3: MINING ALGORITHMS FOR CONTRAST PATTERNS
Description 3: Discuss various algorithms and techniques for mining contrast patterns, focusing on their efficiency and the use of pattern constraints and pruning techniques.

Section 4: USING CONTRAST PATTERNS FOR CLASSIFICATION
Description 4: Explain how contrast patterns can be utilized to build classification models, including strategies for pattern selection and scoring.

Section 5: USING CONTRAST PATTERNS FOR CLUSTERING
Description 5: Describe methodologies for using contrast patterns in clustering, including evaluation measures and the process of forming clusters based on contrast patterns.

Section 6: OTHER APPLICATIONS OF CONTRAST MINING
Description 6: Explore additional applications of contrast mining in various domains such as bioinformatics, blog community analysis, image classification, and subgroup discovery.

Section 7: CONTRAST BASED DATASET DIFFERENCE AND ITEM INTERACTION IN CONTRASTS
Description 7: Examine the methods for measuring dataset differences and analyzing item interactions within contrast patterns.

Section 8: CHALLENGES AND OPEN PROBLEMS
Description 8: Identify current challenges and open research questions in the field of contrast mining, highlighting areas with potential for future research.

Section 9: PREVIEW OF AN UPCOMING BOOK
Description 9: Provide a preview of the upcoming book on contrast data mining, including its structure, contributors, and anticipated publication timeline.