A Survey on Various Data Mining Techniques for ECG Meta Analysis
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paper_title: Fusion of multiple data mining techniques for effective network intrusion detection: a contemporary approach

paper_content:

Today, with more and more computers getting connected to public accessible networks like Internet, computer systems are more and more susceptible to attacks. There is a need of effective intrusion detection systems (IDS) to protect computers from these unauthorized or malicious actions. Different soft-computing based methods have been proposed in recent years for the development of intrusion detection systems. In this paper we are proposing a contemporary approach for network intrusion detection which is significantly improvises the prediction of network intrusions. The proposed technique is a fusion of efficient data mining techniques such as Fuzzy C-means (FCM) clustering, neural network (NN) and Support Vector Machine (SVM). The experiments and evaluations of proposed method were performed with Knowledge Discovery and Data mining (KDD) Cup 99 intrusion detection dataset and we have used sensitivity, specificity, accuracy, precision and F-value as the evaluation metric parameters. Our approach achieved detection accuracy of about 99.96% for DOS attacks, 99.73% for PROBE, 99.93% for R2L and 99.87% for U2R attacks. Our proposed technique yielded very good results and was compared with the other existing techniques to prove its validity.
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paper_title: A sparse fuzzy c-means algorithm based on sparse clustering framework

paper_content:

Abstract Fuzzy c-means (FCM) is a well-known clustering method that has wide applications in statistics, pattern recognition and data mining. However, its performance on large scale and high dimensional data is not satisfactory. In this paper, we propose sparse fuzzy C-means (SFCM) algorithm, which reforms traditional FCM to deal with high dimensional data clustering, based on Witten׳s sparse clustering framework. SFCM embeds feature selection into FCM via sparse weighting and makes model interpretation easier. The experiments and comparisons indicate the method is able to select important features and also increase the efficiency for large-scale clustering problem.
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paper_title: Opinion mining for thai restaurant reviews using K-Means clustering and MRF feature selection

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Opinion mining on millions of Thai restaurant reviews in an unsupervised manner is a challenging task to survey feedbacks of the customers on their products and services. This is extremely helpful for owners to improve their business. In this paper, we propose an opinion mining on Thai restaurant reviews using K-Means clustering and MRF feature selection. The proposed method begins with text preprocessing for breaking reviews into words and removing stop words, followed by text transformation for creating keywords and generating input vectors. MRF feature selection is subsequently adopted for selecting relevant features from a large number of features extracted. Then, K-Means is employed for clustering into positive and negative reviews. From the experimental results, MRF feature selection can efficiently reduce the number of features in the data set so the computational time is significantly decreased. In addition, K-means can achieve the best clustering performance, when compared with Self-Organizing Map, Fuzzy C-Means, and Hierarchical Clustering. Thus, the cooperation of K-means with MRF feature selection is an effective model for clustering Thai restaurant reviews.
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paper_title: Partially supervised k-harmonic means clustering

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A popular algorithm for finding clusters in unlabeled data optimizes the k-means clustering model. This algorithm converges quickly but is sensitive to initialization. Two ways to overcome this drawback are fuzzification and harmonic means. We show that k-harmonic means is a special case of reformulated fuzzy k-means. The main focus of this paper is on partially supervised clustering. Partially supervised clustering finds clusters in data sets that contain both unlabeled and labeled data. We review partially supervised k-means, partially supervised fuzzy k-means, and introduce a partially supervised extension of k-harmonic means. Experiments with four benchmark data sets indicate that partially supervised k-harmonic means inherits the advantages of its completely unsupervised variant: It is significantly less sensitive to initialization than partially supervised k-means.
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paper_title: An evaluation of clustering technique over intrusion detection system

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Data mining has been popularly recognized as an important way to mine useful information from large volumes of data that are noisy, fuzzy & random. Intrusion detection has become an efficient tool against network attack because they allow network administrator to detect vulnerability. Existing IDS techniques includes high false positive and false negative rate. Data mining using IDS reduces the number of false alarm rate. So, here some of the clustering algorithms like k means, hierarchical and Fuzzy C Means have been implemented to analyze the detection rate over KDD CUP 99 dataset. Based on evaluation result, FCM outperforms in terms of both accuracy and computational time.
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paper_title: Random Projection Random Discretization Ensembles—Ensembles of Linear Multivariate Decision Trees

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In this paper, we present a novel ensemble method random projection random discretization ensembles (RPRDE) to create ensembles of linear multivariate decision trees by using a univariate decision tree algorithm. The present method combines the better computational complexity of a univariate decision tree algorithm with the better representational power of linear multivariate decision trees. We develop random discretization (RD) method that creates random discretized features from continuous features. Random projection (RP) is used to create new features that are linear combinations of original features. A new dataset is created by augmenting discretized features (created by using RD) with features created by using RP. Each decision tree of a RPRD ensemble is trained on one dataset from the pool of these datasets by using a univariate decision tree algorithm. As these multivariate decision trees (because of features created by RP) have more representational power than univariate decision trees, we expect accurate decision trees in the ensemble. Diverse training datasets ensure diverse decision trees in the ensemble. We study the performance of RPRDE against other popular ensemble techniques using C4.5 tree as the base classifier. RPRDE matches or outperforms other popular ensemble methods. Experiments results also suggest that the proposed method is quite robust to the class noise.
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paper_title: An improvement to k-nearest neighbor classifier

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K-Nearest neighbor classifier (k-NNC) is simple to use and has little design time like finding k values in k-nearest neighbor classifier, hence these are suitable to work with dynamically varying data-sets. There exists some fundamental improvements over the basic k-NNC, like weighted k-nearest neighbors classifier (where weights to nearest neighbors are given based on linear interpolation), using artificially generated training set called bootstrapped training set, etc. These improvements are orthogonal to space reduction and classification time reduction techniques, hence can be coupled with any of them. The paper proposes another improvement to the basic k-NNC where the weights to nearest neighbors are given based on Gaussian distribution (instead of linear interpolation as done in weighted k-NNC) which is also independent of any space reduction and classification time reduction technique. We formally show that our proposed method is closely related to non-parametric density estimation using a Gaussian kernel. We experimentally demonstrate using various standard data-sets that the proposed method is better than the existing ones in most cases.
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paper_title: Survey on Decision Tree Classification algorithms for the Evaluation of Student Performance

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Now days, the amount of data stored in educational database is increasing rapidly. These databases contain hidden information for improvement of student’s performance. Classification of data objects is a data mining and knowledge management technique used in grouping similar data objects together. There are many classification algorithms available in literature but decision tree is the most commonly used because of its ease of implementation and easier to understand compared to other classification algorithms. The ID3, C4.5 and CART decision tree algorithms has been applied on the data of students to predict their performance. In this paper, all the algorithms are explained one by one. Performance and results are compared of all algorithms and evaluation is done by already existing datasets. All the algorithms has a satisfactory performance but accuracy is more witnessed in case of C4.5 algorithm.
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paper_title: An effective fuzzy C-means algorithm based on symmetry similarity approach

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We propose a novel similarity metric based on the concept of symmetry.The similarity measure is integrated in the conventional fuzzy C-means algorithm.The method shows superior partitioning results in simulations.Qualitative and quantitative analysis verify the effectiveness of the algorithm. Fuzzy C-means (FCM) partitions the observations partially into several clusters based on the principles of fuzzy theory. However, minimization on the Euclidean distance in FCM tends to detect hyper-spherical shaped clusters, which is unfeasible for the real world problems. In this paper, an effective FCM algorithm that adopts the symmetry similarity measure is proposed in order to search for the appropriate clusters, regardless of the geometric structures and overlapping characteristic. Experimental results on several artificial and real life datasets with different nature and the performance assessment with other existing clustering algorithms demonstrate its superiority.
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paper_title: Combining multiple decision trees using fuzzy-neural inference

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This paper presents a novel approach to combining multiple decision trees, which utilizes the power of fuzzy inference techniques and a back-propagation feed forward neural network (BP-FFNN) to improve the overall classification. Crisp multiple decision trees were originally introduced to improve the performance of single decision trees, which offered a restrictive view of the domain. By combining multiple perspectives of the same domain, the information content is increased and the predictive power of the classifier is improved. A predominant weakness in creating multiple crisp trees is the generation of sharp decision boundaries at every node within all trees. The creation of fuzzy decision forests overcomes this weakness by introducing the concepts of fuzzy theory to soften the decision boundaries. To combine information from all fuzzy trees a unique combination of fuzzy inference is used to generate a series of leaf membership grades, which are then used as input to a BP-FFNN. Comparisons are made between using the fuzzy-neural approach and the use of pure fuzzy inference trees and the results indicate that considerable improvement has been made over crisp multiple decision trees.
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Title: A Survey on Various Data Mining Techniques for ECG Meta Analysis

Section 1: INTRODUCTION
Description 1: Introduce the concept of data mining, its importance, and how it is applied to ECG data. Discuss the objectives of the paper and the techniques covered.

Section 2: Association
Description 2: Discuss the association method in data mining and its application to heart disease prediction.

Section 3: Clustering
Description 3: Describe the clustering technique, how it forms groups of similar objects, and its use in predicting heart disease.

Section 4: Classification
Description 4: Explain the classification method based on machine learning, categorizing data into predefined classes using techniques like decision trees and neural networks.

Section 5: Prediction
Description 5: Outline the prediction technique, describing its ability to define relationships between variables and predict future outcomes based on historical data.

Section 6: RELATED WORK
Description 6: Review various studies and papers relevant to the ECG meta-analysis and data mining techniques, summarizing their findings and contributions.

Section 7: REVIEW OF VARIOUS ALGORITHMS STUDIED
Description 7: Provide a detailed review of specific algorithms studied in the context of ECG data analysis, with focus on decision trees and their structure.