Data Mining: Foundations and Practice

Data Mining: Foundations and Practice edited by Tsau Young Lin, Ying Xie, Anita Wasilewska, Churn-Jung Liau.

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 paradigm...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Lin, Tsau Young (Editor), Xie, Ying (Editor), Wasilewska, Anita (Editor), Liau, Churn-Jung (Editor)
Format: eBook
Language:English
Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2008.
Edition:1st ed. 2008.
Series:Studies in Computational Intelligence, 118
Springer eBook Collection.
Subjects:
Applied mathematics.
Engineering mathematics.
Artificial intelligence.
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of Contents:
  • 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.

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