[...]... and selection techniques 3 Data preprocessing: This is required to improve the quality of the actual data for mining This also increases the mining efficiency by reducing the time required for mining the preprocessed data Data preprocessing involves data cleaning, data transformation, data integration, data reduction or data compression for compact representation, etc (a) Data cleaning: It consists... functions of data mining, like classification, clustering, and rule mining, we wish to highlight the current and burning issues related to mining in multimedia applications and Bioinformatics Storage of such huge datasets being more feasible in the compressed domain, we also devote a reasonable portion of the text to data mining in the compressed domain Topics like text mining, image mining, and Web mining. .. heterogeneous data In the remaining part of this chapter we consider data mining from the perspective of machine learning, pattern recognition, image processing, and artificial intelligence We begin by providing the basics of knowledge discovery and data mining in Section 1.2 Sections 1.3-1.7 deal with brief introductions to data compression, information retrieval, text mining, Web mining, and image mining. .. additional steps in the KDD process, such as data preparation, data selection, data cleaning, incorporation of appropriate prior knowledge, and proper interpretation of the results of mining, ensures that useful knowledge is derived from the data Data mining tasks can be descriptive, (i.e., discovering interesting patterns or relationships describing the data) , and predictive (i.e., predicting or classifying... telecommunications, etc Database theories and tools provide the necessary infrastructure to store, access and manipulate data A good overview of KDD can be found in Refs [17] and [18] Data warehousing [2] refers to the current business trends in collecting and cleaning transactional data and making them available for analysis and decision support Data mining works hand in hand with warehouse data Data warehousing... high-dimensional very large databases also influence the performance of data mining systems Data Compression technologies can play a significant role xv xvi PREFACE It is also important that special multimedia data compression techniques are explored especially suitable for data mining applications With the completion of the Human Genome Project, we have access to large databases of biological information... techniques and directions being proposed in the literature everyday In this age of multimedia data exploration, data mining should no longer be restricted to the mining of knowledge from large volumes of high-dimensional datasets in traditional databases only Researchers need to pay attention to the mining of different datatypes, including numeric and alphanumeric formats, text, images, video, voice, speech,... representations only The advanced database management technology of today is enabled to integrate different types of data, such as image, video, text, and other numeric as well as non-numeric data, in a provably single database 2 INTRODUCTION TO DATA MINING in order to facilitate multimedia processing As a result, traditional ad hoc mixtures of statistical techniques and data management tools are no longer... 308 310 311 315 CONTENTS References xiii 315 9 Multimedia Data Mining 9.1 Introduction 9.2 Text Mining 9.2.1 Keyword-based search and mining 9.2.2 Text analysis and retrieval 9.2.3 Mathematical modeling of documents 9.2.4 Similarity-based matching for documents and queries 9.2.5 Latent semantic analysis 9.2.6 Soft computing approaches 9.3 Image Mining 9.3.1 Content-Based Image Retrieval 9.3.2 Color features... in both raw and compressed data domains, fundamentals and principles of classical string matching algorithms, and how all these areas possibly influence data mining and its future growth We cover aspects of advanced image compression, string matching, content based image retrieval, etc., which can influence future developments in data mining, particularly for multimedia data mining There are 10 chapters . Data Mining 1 1.1 Introduction 1 1.2 Knowledge Discovery and Data Mining 5 1.3 Data Compression 10 1.4 Information Retrieval 12 1.5 Text Mining . in Data Mining 70 2.5.1 Regression 71 2.5.2 Association rules 71 2.6 Role of Rough Sets in Data Mining 72 2.7 Role of Wavelets in Data Mining