Data Mining: Foundations and Practice - Lin, Tsau Young; Xie, Ying.; Wasilewska, Anita; Liau, Churn-Jung.; SpringerLink (Online service) (Berlin, Heidelberg : Springer Berlin Heidelberg, 2008)
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.
Compact Representations of Sequential Classification Rules -- An Algorithm for Mining Weighted Dense Maximal 1-Complete Regions -- Mining Linguistic Trends from Time Series -- Latent Semantic Space for Web Clustering -- A Logical Framework for Template Creation and Information Extraction -- A Bipolar Interpretation of Fuzzy Decision Trees -- A Probability Theory Perspective on the Zadeh Fuzzy System -- Three Approaches to Missing Attribute Values: A Rough Set Perspective -- MLEM2 Rule Induction Algorithms: With and Without Merging Intervals -- Towards a Methodology for Data Mining Project Development: The Importance of Abstraction -- Fining Active Membership Functions in Fuzzy Data Mining -- A Compressed Vertical Binary Algorithm for Mining Frequent Patterns -- Naïve Rules Do Not Consider Underlying Causality -- Inexact Multiple-Grained Causal Complexes -- Does Relevance Matter to Data Mining Research? -- E-Action Rules -- Mining E-Action Rules, System DEAR -- Definability of Association Rules and Tables of Critical Frequencies -- Classes of Association Rules: An Overview -- Knowledge Extraction from Microarray Datasets Using Combined Multiple Models to Predict Leukemia Types -- On the Complexity of the Privacy Problem in Databases -- Ensembles of Least Squares Classifiers with Randomized Kernels -- On Pseudo-Statistical Independence in a Contingency Table -- Role of Sample Size and Determinants in Granularity of Contingency Matrix -- Generating Concept Hierarchies from User Queries -- Mining Efficiently Significant Classification Association Rules -- Data Preprocessing and Data Mining as Generalization -- Capturing Concepts and Detecting Concept-Drift from Potential Unbounded, Ever-Evolving and High-Dimensional Data Streams -- A Conceptual Framework of Data Mining -- How to Prevent Private Data from being Disclosed to a Malicious Attacker -- Privacy-Preserving Naive Bayesian Classification over Horizontally Partitioned Data -- Using Association Rules for Classification from Databases Having Class Label Ambiguities: A Belief Theoretic Method.
Keywords: Engineering; Artificial intelligence; Applied mathematics; Engineering mathematics; Engineering; Appl.Mathematics/Computational Methods of Engineering; Artificial Intelligence (incl. Robotics).; Electronic books