Local Pattern Detection: International Seminar Dagstuhl Castle, Germany, April 12-16, 2004, Revised Selected Papers / Edition 1
by Katharina Morik
This collection of 13 selected papers originates from the International Seminar on Local Pattern Detection, held in Dagstuhl Castle, Germany in April 2004.
This state-of-the-art survey on the emerging field Local Pattern Detection addresses four main topics. Three papers cover frequent set mining, four cover subgroup discovery, three cover the statistical view,
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This collection of 13 selected papers originates from the International Seminar on Local Pattern Detection, held in Dagstuhl Castle, Germany in April 2004.
This state-of-the-art survey on the emerging field Local Pattern Detection addresses four main topics. Three papers cover frequent set mining, four cover subgroup discovery, three cover the statistical view, and three papers are devoted to time phenomena.
Product Details
- ISBN-13:
- 9783540265436
- Publisher:
- Springer Berlin Heidelberg
- Publication date:
- 09/01/2005
- Series:
- Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence Series, #3539
- Edition description:
- 2005
- Pages:
- 233
- Product dimensions:
- 0.52(w) x 6.14(h) x 9.21(d)
Table of Contents
Pushing Constraints to Detect Local Patterns.- From Local to Global Patterns: Evaluation Issues in Rule Learning Algorithms.- Pattern Discovery Tools for Detecting Cheating in Student Coursework.- Local Pattern Detection and Clustering.- Local Patterns: Theory and Practice of Constraint-Based Relational Subgroup Discovery.- Visualizing Very Large Graphs Using Clustering Neighborhoods.- Features for Learning Local Patterns in Time-Stamped Data.- Boolean Property Encoding for Local Set Pattern Discovery: An Application to Gene Expression Data Analysis.- Local Pattern Discovery in Array-CGH Data.- Learning with Local Models.- Knowledge-Based Sampling for Subgroup Discovery.- Temporal Evolution and Local Patterns.- Undirected Exception Rule Discovery as Local Pattern Detection.- From Local to Global Analysis of Music Time Series.
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