A Survey on Web Multimedia Mining
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paper_title: Intelligent multimedia information retrieval

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Foreword by Karen Sparck Jones Intelligent multimedia information retrieval lies at the intersection of artificial intelligence, information retrieval, human-computer interaction, and multimedia computing. Its systems enable users to create, process, summarize, present, interact with, and organize information within and across different media such as text, speech, graphics, imagery, and video. These systems go beyond traditional hypermedia and hypertext environments to analyze and generate media, and support intelligent interaction with or via multiple media.The chapters of this volume, which grew out of the 1995 International Joint Conference on Artificial Intelligence Workshop on Intelligent Multimedia Information Retrieval, span a broad range of topics. The book is organized into seven sections: Content-Based Retrieval of Imagery, Content-Based Retrieval of Graphics and Audio, Content-Based Retrieval of Video, Speech and Language Processing for Video Retrieval, Architectures and Tools, Intelligent Hypermedia Retrieval, and Empirical Evaluations.Contributors : Robert Adams, Phillipe Aigrain, Jonathan Ashley, Thom Blum, Shih-Fu Chang, Mei C. Chuah, W. Bruce Croft, Byron Dom, Ann Doubleday, Florence Dubois, Josef Fink, Myron Flickner, Jonathan Foote, Brian Frew, Monika Gorkani, Morgan Green, James Griffioen, Jon Alte Gulla, Jim Hafner, Qian Hang, Matt Hare, Alexander G. Hauptman, Stacie Hibino, Helmut Horacek, David House, Takafumi Inoue, Philippe Joly, Gareth Jones, Karen Sparck Jones, Douglas Keislaer, Stephen Kerpedjiev, Alfred Kobsa, Denis Lee, Veronique Longueville, Chien Yong Low, R. Manmatha, Inderjeet Mani, Mark T. Maybury, Bernard Merialdo, Adrian Muller, Wayne Niblac, Andreas Nill, Alex Pentland, Dragutin Petkovic, Steven F. Roth, Neil C. Rowe, Elke A. Rundensteiner, Harpreet Sawhney, John R. Smith, Stephen W. Smoliar, David Steele, Adelheit Stein, Oliviero Stock, Carlo Strapparava, Alistair Sutcliffe, Atshushi Takeshita, Kazuo Tanaka, Ulrich Thiel, Michele Ryan, Julita Vassileva, James Wheaton, Michael J. Witbrock, Erling Wold, JianHua Wu, Peter Yanker, Rajendra Yavatkar, Steven J. Young, Massimo Zancanaro, HongJiang Zhang
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paper_title: Web Mining Research: A Survey

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With the huge amount of information available online, the World Wide Web is a fertile area for data mining research. The Web mining research is at the cross road of research from several research communities, such as database, information retrieval, and within AI, especially the sub-areas of machine learning and natural language processing. However, there is a lot of confusions when comparing research efforts from different point of views. In this paper, we survey the research in the area of Web mining, point out some confusions regarded the usage of the term Web mining and suggest three Web mining categories. Then we situate some of the research with respect to these three categories. We also explore the connection between the Web mining categories and the related agent paradigm. For the survey, we focus on representation issues, on the process, on the learning algorithm, and on the application of the recent works as the criteria. We conclude the paper with some research issues.
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paper_title: Web Mining Research: A Survey

paper_content:

With the huge amount of information available online, the World Wide Web is a fertile area for data mining research. The Web mining research is at the cross road of research from several research communities, such as database, information retrieval, and within AI, especially the sub-areas of machine learning and natural language processing. However, there is a lot of confusions when comparing research efforts from different point of views. In this paper, we survey the research in the area of Web mining, point out some confusions regarded the usage of the term Web mining and suggest three Web mining categories. Then we situate some of the research with respect to these three categories. We also explore the connection between the Web mining categories and the related agent paradigm. For the survey, we focus on representation issues, on the process, on the learning algorithm, and on the application of the recent works as the criteria. We conclude the paper with some research issues.
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paper_title: Web Mining Research: A Survey

paper_content:

With the huge amount of information available online, the World Wide Web is a fertile area for data mining research. The Web mining research is at the cross road of research from several research communities, such as database, information retrieval, and within AI, especially the sub-areas of machine learning and natural language processing. However, there is a lot of confusions when comparing research efforts from different point of views. In this paper, we survey the research in the area of Web mining, point out some confusions regarded the usage of the term Web mining and suggest three Web mining categories. Then we situate some of the research with respect to these three categories. We also explore the connection between the Web mining categories and the related agent paradigm. For the survey, we focus on representation issues, on the process, on the learning algorithm, and on the application of the recent works as the criteria. We conclude the paper with some research issues.
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paper_title: Mining Knowledge in Noisy Audio Data

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This paper demonstrates a KDD method applied to audio data analysis, particularly, it presents possibilities which result from replacing traditional methods of analysis and acoustic signal processing by KDD algorithms when restoring audio recordings affected by strong noise.
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paper_title: Learning Based Neural Similarity Metrics for Multimedia Data Mining

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Multimedia data mining refers to pattern discovery, rule extraction and knowledge acquisition from multimedia database. Two typical tasks in multimedia data mining are of visual data classification and clustering in terms of semantics. Usually performance of such classification or clustering systems may not be favorable due to the use of low-level features for image representation, and also some improper similarity metrics for measuring the closeness between multimedia objects as well. This paper considers a problem of modeling similarity for semantic image clustering. A collection of semantic images and feed-forward neural networks are used to approximate a characteristic function of equivalence classes, which is termed as a learning pseudo metric (LPM). Empirical criteria on evaluating the goodness of the LPM are established. A LPM based k-Mean rule is then employed for the semantic image clustering practice, where two impurity indices, classification performance and robustness are used for performance evaluation. An artificial image database with 11 semantics is employed for our simulation studies. Results demonstrate the merits and usefulness of our proposed techniques for multimedia data mining.
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paper_title: MultiMediaMiner: a system prototype for multimedia data mining

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Multimedia data mining is the mining of high-level multimedia information and knowledge from large multimedia databases. A multimedia data mining system prototype, MultiMediaMiner, has been designed and developed. It includes the construction of a multimedia data cube which facilitates multiple dimensional analysis of multimedia data, primarily based on visual content, and the mining of multiple kinds of knowledge, including summarization, comparison, classification, association , and clustering .
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paper_title: Using Artificial Intelligence Planning to Automate Science Data Analysis for Large Image Databases

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This paper describes the use of AI planning techniques to represent scientific, image processing, and software tool knowledge to automate knowledge discovery and data mining (e.g., science data analysis) of large image databases. In particular, we describe two fielded systems. The Multimission VICAR Planner (MVP) which has been deployed for 2 years and is currently supporting science product generation for the Galileo mission. MVP has reduced time to fil certain classes of requests from 4 hours to 15 minutes. The Automated SAR Image Processing system (ASIP) which is currently in use by the Dept. of Geology at ASU supporting aeolian science analysis of synthetic aperture radar images. ASIP reduces the number of manual inputs in science product generation by 10-fold.
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paper_title: Managing and Mining Multimedia Databases

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From the Publisher: ::: There is now so much data on the Web that managing it with conventional tools is becoming almost impossible. To manage this data, provide interoperability and warehousing between multiple data sources and systems, and extract information from the databases and warehouses, various tools are being developed. In fact, developments in multimedia database management have exploded during the past decade. To date, however, there has been little information available on providing a complete set of services for multimedia databases, including their management, mining, and integration on the Web for electronic enterprises.Managing and Mining Multimedia Databases fills that gap. Focusing on managing and mining multimedia databases for electronic commerce and business, it explores database management system techniques for text, image, audio, and video databases. It addresses the issues and challenges of mining multimedia database to extract information, and discusses the directions and challenges related to integrating multimedia databases for the Web, particularly for e-business.This book provides a comprehensive overview of multimedia data management and mining technologies, from the underlying concepts, architectures, and data models for multimedia database systems to the technologies that support multimedia data management on the Web, privacy issues, and emerging standards, prototypes, and products. Designed for technical managers, executives, and technologists, it offers your only opportunity to learn about both multimedia data management and multimedia data mining within a single book.
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paper_title: A non-classical logic for information retrieval

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This paper is to be seen as describing a new theoretical framework for investigating information retrieval. For some years now, I have felt the need to describe such a framework. It is especially important if one wants to develop information retrieval beyond the mere keyword approach. In the closing pages of my earlier book on the subject I said the following: “It has never been assumed that a retrieval system should attempt to understand” the content of a document. Most Information Retrieval systems at the moment merely aim at a bibliographic search. Documents are deemed to be relevant on the basis of a superficial description. I do not suggest that it is going to be a simple matter to program a computer to understand documents. What is suggested is that some attempt should be made to construct something like a naive model, using more than just keywords, of the content of each document in the system. The more sophisticated question-answering systems do something very similar. They have a model of their universe of discourse and can answer questions about it, and can incorporate new facts and rules as they become available (van Rijsbergen, 1979).
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paper_title: A vector space model for automatic indexing

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In a document retrieval, or other pattern matching environment where stored entities (documents) are compared with each other or with incoming patterns (search requests), it appears that the best indexing (property) space is one where each entity lies as far away from the others as possible; in these circumstances the value of an indexing system may be expressible as a function of the density of the object space; in particular, retrieval performance may correlate inversely with space density. An approach based on space density computations is used to choose an optimum indexing vocabulary for a collection of documents. Typical evaluation results are shown, demonstating the usefulness of the model.
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paper_title: An Information-Driven Framework for Image Mining

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Image mining systems that can automatically extract semantically meaningful information (knowledge) from image data are increasingly in demand. The fundamental challenge in image mining is to determine how lowlevel, pixel representation contained in a raw image or image sequence can be processed to identify high-level spatial objects and relationships. To meet this challenge, we propose an efficient information-driven framework for image mining. We distinguish four levels of information: the Pixel Level, the Object Level, the Semantic Concept Level, and the Pattern and Knowledge Level. High-dimensional indexing schemes and retrieval techniques are also included in the framework to support the flow of information among the levels. We believe this framework represents the first step towards capturing the different levels of information present in image data and addressing the issues and challenges of discovering useful patterns/knowledge from each level.
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paper_title: Image Mining: Issues, Frameworks and Techniques

paper_content:

Advances in image acquisition and storage technology have led to tremendous growth in significantly large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. Despite the development of many applications and algorithms in the individual research fields cited above, research in image mining is still in its infancy. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining at the end of this paper.
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paper_title: Feature Extraction Construction and Selection: A Data Mining Perspective

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From the Publisher: ::: The book can be used by researchers and graduate students in machine learning, data mining, and knowledge discovery, who wish to understand techniques of feature extraction, construction and selection for data pre-processing and to solve large size, real-world problems. The book can also serve as a reference book for those who are conducting research about feature extraction, construction and selection, and are ready to meet the exciting challenges ahead of us.
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paper_title: A novel vector-based approach to color image retrieval using a vector angular-based distance measure

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Color is the characteristic which is most used for image indexing and retrieval. Due to its simplicity, the color histogram remains the most commonly used method for this task. However, the lack of good perceptual histogram similarity measures, the global color content of histograms, and the erroneous retrieval results due to gamma nonlinearity, call for improved methods. We present a new scheme which implements a recursive HSV-space segmentation technique to identify perceptually prominent color areas. The average color vector of these extracted areas are then used to build the image indices, requiring very little storage. Our retrieval is performed by implementing a combination distance measure, based on the vector angle between two vectors. Our system provides accurate retrieval results and high retrieval rate. It allows for queries based on single or multiple colors and, in addition, it allows for certain colors to be excluded in the query. This flexibility is due to our distance measure and the multidimensional query space in which the retrieval ranking of the database images is determined. Furthermore, our scheme proves to be very resistant to gamma nonlinearity providing robust retrieval results for a wide range of gamma nonlinearity values, which proves to be of great importance since, in general, the image acquisition source is unknown.
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paper_title: Color Image Clustering using Block Truncation Algorithm

paper_content:

With the advancement in image capturing device, the image data been generated at high volume. If images are analyzed properly, they can reveal useful information to the human users. Content based image retrieval address the problem of retrieving images relevant to the user needs from image databases on the basis of low-level visual features that can be derived from the images. Grouping images into meaningful categories to reveal useful information is a challenging and important problem. Clustering is a data mining technique to group a set of unsupervised data based on the conceptual clustering principal: maximizing the intraclass similarity and minimizing the interclass similarity. Proposed framework focuses on color as feature. Color Moment and Block Truncation Coding (BTC) are used to extract features for image dataset. Experimental study using K-Means clustering algorithm is conducted to group the image dataset into various clusters.
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paper_title: Image Classification Approach Based on Manifold Learning in Web Image Mining

paper_content:

Automatic image classification is a challenging research topic in Web image mining. In this paper, we formulate image classification problem as the calculation of the distance measure between training manifold and test manifold. We propose an improved nonlinear dimensionality reduction algorithm based on neighborhood optimization, not only to decrease feature dimensionality but also to transform the problem from high-dimensional data space into low-dimensional feature space. Considering that the images in most real-world applications have large diversities within category and among categories, we propose a new scheme to construct a set of training manifolds each representing one semantic category and partition each nonlinear manifold into several linear sub-manifolds via region growing. Moreover, to further reduce computational complexity, each sub-manifold is depicted by aggregation center. Experimental results on two Web image sets demonstrate the feasibility and effectiveness of the proposed approach.
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paper_title: An Information-Driven Framework for Image Mining

paper_content:

Image mining systems that can automatically extract semantically meaningful information (knowledge) from image data are increasingly in demand. The fundamental challenge in image mining is to determine how lowlevel, pixel representation contained in a raw image or image sequence can be processed to identify high-level spatial objects and relationships. To meet this challenge, we propose an efficient information-driven framework for image mining. We distinguish four levels of information: the Pixel Level, the Object Level, the Semantic Concept Level, and the Pattern and Knowledge Level. High-dimensional indexing schemes and retrieval techniques are also included in the framework to support the flow of information among the levels. We believe this framework represents the first step towards capturing the different levels of information present in image data and addressing the issues and challenges of discovering useful patterns/knowledge from each level.
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paper_title: Web Image Mining toward Generic Image Recognition.

paper_content:

We describe a generic image classification system with an automatic knowledge acquisition mechanism from the Web. The processing in the system consists of three steps. In the gathering stage, the system gathers images related to given class keywords from the Web automatically. In the learning stage, it extracts image features from gathered images and associates them with each class. In the classification stage, the system classifies an unknown image into one of the classes corresponding to the class keywords by using the association between the image features and the classes.
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paper_title: Image Mining: Issues, Frameworks and Techniques

paper_content:

Advances in image acquisition and storage technology have led to tremendous growth in significantly large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. Despite the development of many applications and algorithms in the individual research fields cited above, research in image mining is still in its infancy. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining at the end of this paper.
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paper_title: Multi-Modal Mining in Web image retrieval

paper_content:

The associations between different modalities of Web images could be very useful for Web image retrieval. In this paper, we investigate the multi-modal associations between two basic modalities of web images, i.e. keyword and visual feature clusters, by data mining technique. The association rule crosses two modalities, in which the antecedent is a single keyword and the consequent is several visual feature clusters. A customized mining process is provided to mine such special multi-modal association rules. The multi-modal association rules are obtained offline based on the existing inverted file and utilized online to automatically integrate the keyword and visual features for web image retrieval. The experiments are carried out in a prototype system for Web image retrieval, and the results show the effectiveness of the mined multi-modal association rules.
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paper_title: Web image mining towards universal age estimator

paper_content:

In this paper, we present an automatic web image mining system towards building a universal human age estimator based on facial information, which is applicable to all ethnic groups and various image qualities. First, a large (
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paper_title: Image Mining using Content Based Image Retrieval System

paper_content:

The image depends on the Human perception and is also based on the Machine Vision System. The Image Retrieval is based on the color Histogram, texture. The perception of the Human System of Image is based on the Human Neurons which hold the 10 12 of Information; the Human brain continuously learns with the sensory organs like eye which transmits the Image to the brain which interprets the Image. The research challenge is that how the brain processes the information in the semantic manner is hot area of research till date. Hence we can say that a single set of Algorithm cannot define the complete set of operations on the field of Image Mining. This Paper examines the State-of - art technology-Image mining techniques which is based on the Color Histogram, texture of him Image. The query Image is taken then the Color Histogram and Texture is taken and based on this the resultant Image is output.
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paper_title: A visual search system for video and image databases

paper_content:

This paper describes a new methodology for image search that is applicable to both image libraries and keyframe search over video libraries. In contrast to previous approaches, which require templates or direct manipulation of low-level image parameters, our search system classifies images into a pre-defined subject lexicon, including terms such as trees and flesh tones. The classification is performed off-line using neural network algorithms. Query satisfaction is performed on-line using only the image tags. Because most of the work is done off-line, this methodology answers queries much more quickly than techniques that require direct manipulation of images to answer the query. We also believe that pre-defined subjects are easier for users to understand when searching programmatic video material. Experiments using keyframes extracted by our video library system show that the methodology gives high-quality query results with fast on-line performance.
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paper_title: Content-based classification, search, and retrieval of audio

paper_content:

Many audio and multimedia applications would benefit from the ability to classify and search for audio based on its characteristics. The audio analysis, search, and classification engine described here reduces sounds to perceptual and acoustical features. This lets users search or retrieve sounds by any one feature or a combination of them, by specifying previously learned classes based on these features, or by selecting or entering reference sounds and asking the engine to retrieve similar or dissimilar sounds.
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paper_title: An Algorithm to Estimate Mean Traffic Speed Using Uncalibrated Cameras

paper_content:

We present a novel approach to estimate traffic speed using a sequence of images from an uncalibrated camera. We assert that exact calibration is not necessary to estimate speed. Instead, we use: 1) geometric relationships inherently available in the image, 2) some common-sense assumptions that reduce the problem to a one-dimensional geometry, 3) frame differencing to isolate moving edges and track vehicles between frames, and 4) parameters from the distribution of vehicle lengths to estimate speed.
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paper_title: Fast Spatio-Temporal Data Mining of Large Geophysical Datasets

paper_content:

The important scientific challenge of understanding global climate change is one that clearly requires the application of knowledge discovery and datamining techniques on a massive scale. Advances in parallel supercomputing technology, enabling high-resolution modeling, as well as in sensor technology, allowing data capture on an unprecedented scale, conspire to overwhelm present-day analysis approaches. We present here early experiences with a prototype exploratory data analysis environment, CONQUEST, designed to provide content-based access to such massive scientific datasets. CONQUEST (CONtent-based Querying in Space and Time) employs a combination of workstations and massively parallel processors (MPP's) to mine geophysical datasets possessing a prominent temporal component. It is designed to enable complex multi-modal interactive querying and knowledge discovery, while simultaneously coping with the extraordinary computational demands posed by the scope of the datasets involved. After outlining a working prototype, we concentrate here on the description of several associated feature extraction algorithms implemented on MPP platforms, together with some typical results.
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paper_title: Separate-and-Conquer Rule Learning

paper_content:

This paper is a survey of inductive rule learning algorithms that use a separate-and-conquer strategy. This strategy can be traced back to the AQ learning system and still enjoys popularity as can be seen from its frequent use in inductive logic programming systems. We will put this wide variety of algorithms into a single framework and analyze them along three different dimensions, namely their search, language and overfitting avoidance biases.
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paper_title: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods

paper_content:

From the publisher: This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. SVMs deliver state-of-the-art performance in real-world applications such as text categorisation, hand-written character recognition, image classification, biosequences analysis, etc., and are now established as one of the standard tools for machine learning and data mining. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and its applications. The concepts are introduced gradually in accessible and self-contained stages, while the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software ensure that it forms an ideal starting point for further study. Equally, the book and its associated web site will guide practitioners to updated literature, new applications, and on-line software.
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paper_title: Artificial Neural Network Learning: A Comparative Review

paper_content:

Various neural learning procedures have been proposed by different researchers in order to adapt suitable controllable parameters of neural network architectures. These can be from simple Hebbian procedures to complicated algorithms applied to individual neurons or assemblies in a neural structure. The paper presents an organized review of various learning techniques, classified according to basic characteristics such as chronology, applicability, functionality, stochasticity etc. Some of the learning procedures that have been used for the training of generic and specific neural structures, and will be reviewed are: Hebbian-like (Grossberg, Sejnowski, Sutton, Bienenstock, Oja & Karhunen, Sanger, Yuile et al., Hasselmo, Kosko, Cheung & Omidvar), Reinforcement learning, Min-max learning, Stochastic learning, Genetics-based learning, Artificial life-based learning. The various learning procedures will be critically compared, and future trends will be highlighted.
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paper_title: Data clustering: a review

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Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overviewof pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval.
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paper_title: Generalized Affinity-Based Association Rule Mining for Multimedia Database Queries

paper_content:

The recent progress in high-speed communication networks and large-capacity storage devices has led to a tremendous increase in the number of databases and the volume of data in them. This has created a need to discover structural equivalence relationships from the databases since queries tend to access information from structurally equivalent media objects residing in different databases. The more databases there are, the more query-processing performance improvement can be achieved when the structural equivalence relationships are automatically discovered. In response to such a demand, association rule mining has emerged and proven to be a highly successful technique for discovering knowledge from large databases. In this paper, we explore a generalized affinity-based association rule mining approach to discover the quasi-equivalence relationships from a network of databases. The algorithm is implemented and two empirical studies on real databases are conducted. The results show that the proposed generalized affinity-based association rule mining approach not only correctly exploits the set of quasi-equivalent media objects from the databases, but also outperforms the basic association rule mining approach in the discovery of the quasi-equivalent media object pairs.
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Title: A Survey on Web Multimedia Mining

Section 1: INTRODUCTION
Description 1: Introduce the concept of web multimedia mining and its interdisciplinary nature, highlighting the need and importance of extracting useful knowledge from web multimedia data.

Section 2: WEB MINING TAXONOMY
Description 2: Describe the distinct categories of web mining: web content mining, web structure mining, and web usage mining, focusing on the kinds of data to be mined.

Section 3: Web Content Mining
Description 3: Explain the process of extracting useful information from the contents of web documents, including text, images, audio, and video.

Section 4: Web Structure Mining
Description 4: Discuss the process of discovering structural information from the web, including hyperlinks and document structure.

Section 5: Web Usage Mining
Description 5: Detail the application of data mining techniques to discover usage patterns from web data to enhance web-based applications.

Section 6: MINING MULTIMEDIA DATABASES
Description 6: Overview of techniques for representing, storing, indexing, and retrieving multimedia data and the data mining techniques used in this context.

Section 7: Text Mining
Description 7: Describe the state of the art in text mining, including text representation, classification, clustering, and information extraction.

Section 8: Image Mining
Description 8: Discuss the processes and challenges in detecting patterns and extracting useful information from images in large databases.

Section 9: Video Mining
Description 9: Explain the complexities of mining video data and techniques for query and retrieval from video databases.

Section 10: Audio Mining
Description 10: Outline the techniques for processing and mining audio data, including speech transcription and direct audio data mining.

Section 11: PROCESS OF APPLICATION OF MULTIMEDIA MINING
Description 11: Detail the steps involved in applying multimedia mining to different media types, including data collection, pre-processing, and learning models.

Section 12: Applications and Systems
Description 12: Provide examples of applications and systems where multimedia mining is utilized, such as satellite data analysis, video, and audio data mining.

Section 13: MODELS FOR MULTIMEDIA MINING
Description 13: Discuss the models used for multimedia mining, including classification, clustering, and association rule mining.

Section 14: CONCLUSION
Description 14: Summarize the key points and highlight the gaps and opportunities in the field of multimedia mining.