Local Pattern Detection: International Seminar Dagstuhl Castle, Germany, April 12-16, 2004, Revised Selected Papers / Edition 1

by Katharina Morik, Arno Siebes, Jean-Francois Boulicaut

Overview

Introduction The dramatic increase in available computer storage capacity over the last 10 years has led to the creation of very large databases of scientific and commercial information. The need to analyze these masses of data has led to the evolution of the new field knowledge discovery in databases (KDD) at the intersection of machine learning, statistics and database technology. Being interdisciplinary by nature, the field offers the opportunity to combine the expertise of different fields into a common objective.Moreover,within each field diverse methods have been developed and justified with respect to different quality criteria. We have toinvestigatehowthesemethods can contribute to solving the problem of KDD. Traditionally, KDD was seeking to find global models for the data that - plain most of the instances of the database and describe the general structure of the data. Examples are statistical time series models, cluster models, logic programs with high coverage or classification models like decision trees or linear decision functions. In practice, though, the use of these models often is very limited, because global models tend to find only the obvious patterns in the data, 1 which domain experts already are aware of. What is really of interest to the users are the local patterns that deviate from the already-known background knowledge. David Hand, who organized a workshop in 2002, proposed the new field of local patterns.

Product Details

ISBN-13:	9783540265436
Publisher:	Springer Berlin Heidelberg
Publication date:	09/01/2005
Series:	Lecture Notes in Computer Science , #3539
Edition description:	2005
Pages:	233
Product dimensions:	6.10(w) x 9.25(h) x 0.36(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.