Lin T.Y. et al. (Eds.) Data Mining: Foundations and Practice

Springer, 2008. — 562 p. — (Studies in Computational Intelligence 118). — ISBN: 354078487X.This book contains valuable studies in data mining from both foundational and practical perspectives. The foundational studies of data mining may help to lay a solid foundation for data mining as a scientific discipline, while the practical studies of data mining may lead to new data mining paradigms and algorithms. The foundational studies contained in this book focus on a broad range of subjects, including conceptual framework of data mining, data preprocessing and data mining as generalization, probability theory perspective on fuzzy systems, rough set methodology on missing values, inexact multiple-grained causal complexes, complexity of the privacy problem, logical framework for template creation and information extraction, classes of association rules, pseudo statistical independence in a contingency table, and role of sample size and determinants in granularity of contingency matrix. The practical studies contained in this book cover different fields of data mining, including rule mining, classification, clustering, text mining, Web mining, data stream mining, time series analysis, privacy preservation mining, fuzzy data mining, ensemble approaches, and kernel based approaches. We believe that the works presented in this book will encourage the study of data mining as a scientific field and spark collaboration among researchers and practitioners.Elena Baralis, Silvia Chiusano, Riccardo Dutto, and Luigi Mantellini
Compact Representations of Sequential Classification Rules
Haiyun Bian and Raj Bhatnagar
An Algorithm for Mining Weighted Dense Maximal 1-Complete Regions
Chun-Hao Chen, Tzung-Pei Hong, and Vincent S. Tseng
Mining Linguistic Trends from Time Series
I-Jen Chiang, Tsau Young (‘T. Y.’) Lin, Hsiang-Chun Tsai, Jau-Min Wong, and Xiaohua Hu
Latent Semantic Space for Web Clustering
David Corney, Emma Byrne, Bernard Buxton, and David Jones
A Logical Framework for Template Creation and Information Extraction
Tuan-Fang Fan, Churn-Jung Liau, and Duen-Ren Liu
A Bipolar Interpretation of Fuzzy Decision Trees
Qing Shi Gao, Xiao Yu Gao, and Lei Xu
A Probability Theory Perspective on the Zadeh Fuzzy System
Jerzy W. Grzymala-Busse
Three Approaches to Missing Attribute Values: A Rough Set Perspective
Jerzy W. Grzymala-Busse
MLEM2 Rule Induction Algorithms: With and Without Merging Intervals
P. Gonz´alez-Aranda, E. Menasalvas, S. Mill´an, Carlos Ruiz, and J. Segovia
Towards a Methodology for Data Mining Project Development: The Importance of Abstraction
Tzung-Pei Hong, Chun-Hao Chen, Yu-Lung Wu, and Vincent S. Tseng
Fining Active Membership Functions in Fuzzy Data Mining
J. Hdez. Palancar, R. Hdez. León, J. Medina Pagola, and A. Hechavarría
A Compressed Vertical Binary Algorithm for Mining Frequent Patterns
Lawrence J. Mazlack
Naїve Rules Do Not Consider Underlying Causality
Lawrence J. Mazlack
Inexact Multiple-Grained Causal Complexes
Mykola Pechenizkiy, Seppo Puuronen, and Alexey Tsymbal
Does Relevance Matter to Data Mining Research?
Li-Shiang Tsay and Zbigniew W. Rás
E-Action Rules
Zbigniew W. Rás and Li-Shiang Tsay
Mining E-Action Rules, System DEAR
Jan Rauch
Definability of Association Rules and Tables of Critical Frequencies
Jan Rauch
Classes of Association Rules: An Overview
Gregor Stiglic, Nawaz Khan, and Peter Kokol
Knowledge Extraction from Microarray Datasets Using Combined Multiple Models to Predict Leukemia Types
Bhavani Thuraisingham
On the Complexity of the Privacy Problem in Databases
Kari Torkkola and Eugene Tuv
Ensembles of Least Squares Classifiers with Randomized Kernels
Shusaku Tsumoto
On Pseudo-Statistical Independence in a Contingency Table
Shusaku Tsumoto
Role of Sample Size and Determinants in Granularity of Contingency Matrix
Bob Wall, Neal Richter, and Rafal Angryk
Generating Concept Hierarchies from User Queries
Yanbo J. Wang, Qin Xin, and Frans Coenen
Mining Efficiently Significant Classification Association Rules
Anita Wasilewska and Ernestina Menasalvas
Data Preprocessing and Data Mining as Generalization
Ying Xie, Ajay Ravichandran, Hisham Haddad, and Katukuri Jayasimha
Capturing Concepts and Detecting Concept-Drift from Potential Unbounded, Ever-Evolving and High-Dimensional Data Streams
Yiyu Yao, Ning Zhong, and Yan Zhao
A Conceptual Framework of Data Mining
Justin Zhan, LiWu Chang, and Stan Matwin
How to Prevent Private Data from being Disclosed to a Malicious Attacker
Justin Zhan, Stan Matwin, and LiWu Chang
Privacy-Preserving Naive Bayesian Classification over Horizontally Partitioned Data
S.P. Subasingha, J. Zhang, K. Premaratne, M.-L. Shyu, M. Kubat, and K.K.R.G.K. Hewawasam
Using Association Rules for Classification from Databases Having Class Label Ambiguities: A Belief Theoretic Method