Detecting groups of anomalously similar objects in large data sets

Authors:
Zhicheng Zhang;David J. Hand
Affiliations:
Department of Mathematics, Imperial College London, London, UK;Department of Mathematics, Imperial College London, London, UK
Venue:
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Year:
2005

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Abstract

Pattern discovery is a facet of data mining concerned with the detection of ”small local” structures in large data sets. In high dimensions this is typically difficult because of the computational work involved in searching over the data space. In this paper we outline a tool called PEAKER which can detect patterns efficiently in high dimensions. We approach the subject through the two aspects of pattern discovery, detection and verification. We demonstrate various ways of using PEAKER as well as its various inherent properties, emphasizing the exploratory nature of the tool.