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Free ebooks
 Free database ebooks Other database ebooks IGI Publishing-Data Mining Patterns: New Methods and Applications
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 Chapter I Metric Methods in Data MiningThis chapter presents data mining techniques that make use of metrics defined on the set of partitions of finite sets. Chapter II Bi-Directional Constraint Pushing in Frequent Pattern Mining Frequent itemset mining (FIM) is a key component of many algorithms that extract patterns from transactional databases. Chapter III Mining Hyperclique Patterns: A Summary of Results This chapter presents a framework for mining highly correlated association patterns named hyperclique patterns. Chapter IV Pattern Discovery in Biosequences: From Simple to Complex Patterns The aim of this chapter is to explain how pattern discovery can be applied to deal with such important biological problems, describing also a number of relevant techniques proposed in the literature. Chapter V Finding Patterns in Class-Labeled Data Using Data Visualization The chapter describes a method called VizRank, which can be used to automatically identify interesting data projections for multivariate visualizations of class-labeled data. Chapter VI Summarizing Data Cubes Using Blocks In this chapter, we present a novel approach allowing to build automatically blocks of similar values in a given data cube that are meant to summarize the content of the cube. Chapter VII Social Network Mining from the Web This chapter describes social network mining from the Web. Since the end of the 1990’s, several attempts have been made to mine social network information from e-mail messages, message boards, Web linkage structure, and Web content. Chapter VIII Discovering Spatio-Textual Association Rules in Document Images This chapter introduces a data mining method for the discovery of association rules from images of scanned paper documents. Chapter IX Mining XML Documents This chapter describes various ways of using and simplifying this tree structure to model documents and support efficient mining algorithms. Chapter X Topic and Cluster Evolution Over Noisy Document Streams Chapter IX Discovery of Latent Patterns with Hierarchical Bayesian Mixed-Membership Models and the Issue of Model Choice
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