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More About This Textbook
Overview
This book constitutes the thoroughly refereed postproceedings of the Second International Workshop on Active Mining, AM 2003, held in Maebashi, Japan, in October 2003 as a satellite workshop of ISMIS 2003.
The 16 revised full papers presented together with 2 tutorial papers and an overview of the Japanese Active Mining Project went through 2 rounds of reviewing and improvement and were selected from initialy 38 submissions. The papers are organized in topical sections on active information collection, active data mining, and active user reaction. Many current aspects of active mining are addressed, ranging from theoretical and methodological topics to algorithms and their applications in fields such as bioinformatics, medicine, and life science more generally.
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Table of Contents
Overview.- Active Mining Project: Overview.- Tutorial Papers.- Computational and Statistical Methods in Bioinformatics.- Indexing and Mining Audiovisual Data.- Active Information Collection.- Relevance Feedback Document Retrieval Using Support Vector Machines.- Micro View and Macro View Approaches to Discovered Rule Filtering.- Mining Chemical Compound Structure Data Using Inductive Logic Programming.- First-Order Rule Mining by Using Graphs Created from Temporal Medical Data.- Active Data Mining.- Extracting Diagnostic Knowledge from Hepatitis Dataset by Decision Tree Graph-Based Induction.- Data Mining Oriented CRM Systems Based on MUSASHI: C-MUSASHI.- Investigation of Rule Interestingness in Medical Data Mining.- Experimental Evaluation of Time-Series Decision Tree.- Spiral Multi-aspect Hepatitis Data Mining.- Sentence Role Identification in Medline Abstracts: Training Classifier with Structured Abstracts.- CHASE 2 – Rule Based Chase Algorithm for Information Systems of Type—.- Active User Reaction.- Empirical Comparison of Clustering Methods for Long Time-Series Databases.- Spiral Mining Using Attributes from 3D Molecular Structures.- Classification of Pharmacological Activity of Drugs Using Support Vector Machine.- Cooperative Scenario Mining from Blood Test Data of Hepatitis B and C.- Integrated Mining for Cancer Incidence Factors from Healthcare Data.