Detecting groups of anomalously similar objects in large data sets

  • Authors:

  • Zhicheng Zhang;David J. Hand

  • Affiliations:

  • Department of Mathematics, Imperial College London, 180 Queens Gate, Huxley Building, London, SW7 2AZ, UK. E-mail: {zhzhang,d.j.hand}@imperial.ac.uk;Department of Mathematics, Imperial College London, 180 Queens Gate, Huxley Building, London, SW7 2AZ, UK. E-mail: {zhzhang,d.j.hand}@imperial.ac.uk

  • Venue:
  • Intelligent Data Analysis - Selected papers from IDA2005, Madrid, Spain
  • Year:
  • 2006

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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.