Principles of data mining and knowledge discovery : 5th European Conference, PKDD 2001, Freiburg, Germany, September 3-5, 2001 : proceedings
著者
書誌事項
Principles of data mining and knowledge discovery : 5th European Conference, PKDD 2001, Freiburg, Germany, September 3-5, 2001 : proceedings
(Lecture notes in computer science, 2168 . Lecture notes in artificial intelligence)
Springer, c2001
大学図書館所蔵 全37件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
Includes bibliographical references and index
内容説明・目次
内容説明
This book constitutes the refereed proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery, PKDD 2001, held in Freiburg, Germany, in September 2001.
The 40 revised full papers presented together with four invited contributions were carefully reviewed and selected from close to 100 submissions. Among the topics addressed are hidden Markov models, text summarization, supervised learning, unsupervised learning, demographic data analysis, phenotype data mining, spatio-temporal clustering, Web-usage analysis, association rules, clustering algorithms, time series analysis, rule discovery, text categorization, self-organizing maps, filtering, reinforcemant learning, support vector machines, visual data mining, and machine learning.
目次
Regular Papers.- Self-Similar Layered Hidden Markov Models.- Automatic Text Summarization Using Unsupervised and Semi-supervised Learning.- Detecting Temporal Change in Event Sequences: An Application to Demographic Data.- Knowledge Discovery in Multi-label Phenotype Data.- Computing Association Rules Using Partial Totals.- Gaphyl: A Genetic Algorithms Approach to Cladistics.- Parametric Approximation Algorithms for High-Dimensional Euclidean Similarity.- Data Structures for Minimization of Total Within-Group Distance for Spatio-temporal Clustering.- Non-crisp Clustering by Fast, Convergent, and Robust Algorithms.- Pattern Extraction for Time Series Classification.- Specifying Mining Algorithms with Iterative User-Defined Aggregates: A Case Study.- Interesting Fuzzy Association Rules in Quantitative Databases.- Interestingness Measures for Fuzzy Association Rules.- A Data Set Oriented Approach for Clustering Algorithm Selection.- Fusion of Meta-knowledge and Meta-data for Case-Based Model Selection.- Discovery of Temporal Patterns.- Temporal Rule Discovery for Time-Series Satellite Images and Integration with RDB.- Using Grammatical Inference to Automate Information Extraction from the Web.- Biological Sequence Data Mining.- Implication-Based Fuzzy Association Rules.- A General Measure of Rule Interestingness.- Error Correcting Codes with Optimized Kullback-Leibler Distances for Text Categorization.- Propositionalisation and Aggregates.- Algorithms for the Construction of Concept Lattices and Their Diagram Graphs.- Data Reduction Using Multiple Models Integration.- Discovering Fuzzy Classification Rules with Genetic Programming and Co-evolution.- Sentence Filtering for Information Extraction in Genomics, a Classification Problem.- Text Categorization and Semantic Browsing with Self-Organizing Maps on Non-euclidean Spaces.- A Study on the Hierarchical Data Clustering Algorithm Based on Gravity Theory.- Internet Document Filtering Using Fourier Domain Scoring.- Distinguishing Natural Language Processes on the Basis of fMRI-Measured Brain Activation.- Automatic Construction and Refinement of a Class Hierarchy over Multi-valued Data.- Comparison of Three Objective Functions for Conceptual Clustering.- Identification of ECG Arrhythmias Using Phase Space Reconstruction.- Finding Association Rules That Trade Support Optimally against Confidence.- Bloomy Decision Tree for Multi-objective Classification.- Discovery of Temporal Knowledge in Medical Time-Series Databases Using Moving Average, Multiscale Matching, and Rule Induction.- Mining Positive and Negative Knowledge in Clinical Databases Based on Rough Set Model.- The TwoKey Plot for Multiple Association Rules Control.- Lightweight Collaborative Filtering Method for Binary-Encoded Data.- Invited Papers.- Support Vectors for Reinforcement Learning.- Combining Discrete Algorithmic and Probabilistic Approaches in Data Mining.- Statistification or Mystification? The Need for Statistical Thought in Visual Data Mining.- The Musical Expression Project: A Challenge for Machine Learning and Knowledge Discovery.- Scalability, Search, and Sampling: From Smart Algorithms to Active Discovery.
「Nielsen BookData」 より