Compression-based data mining of sequential data

  • Authors:

  • Eamonn Keogh;Stefano Lonardi;Chotirat Ann Ratanamahatana;Li Wei;Sang-Hee Lee;John Handley

  • Affiliations:

  • Department of Computer Science and Engineering, University of California, Riverside, USA 92521;Department of Computer Science and Engineering, University of California, Riverside, USA 92521;Department of Computer Engineering, Chulalongkorn University, Bangkok, Thailand;Department of Computer Science and Engineering, University of California, Riverside, USA 92521;Department of Anthropology, University of California, Riverside, USA 92521;Xerox Innovation Group, Xerox Corporation, New York, USA 14580-9701

  • Venue:
  • Data Mining and Knowledge Discovery
  • Year:
  • 2007

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Abstract

The vast majority of data mining algorithms require the setting of many input parameters. The dangers of working with parameter-laden algorithms are twofold. First, incorrect settings may cause an algorithm to fail in finding the true patterns. Second, a perhaps more insidious problem is that the algorithm may report spurious patterns that do not really exist, or greatly overestimate the significance of the reported patterns. This is especially likely when the user fails to understand the role of parameters in the data mining process. Data mining algorithms should have as few parameters as possible. A parameter-light algorithm would limit our ability to impose our prejudices, expectations, and presumptions on the problem at hand, and would let the data itself speak to us. In this work, we show that recent results in bioinformatics, learning, and computational theory hold great promise for a parameter-light data-mining paradigm. The results are strongly connected to Kolmogorov complexity theory. However, as a practical matter, they can be implemented using any off-the-shelf compression algorithm with the addition of just a dozen lines of code. We will show that this approach is competitive or superior to many of the state-of-the-art approaches in anomaly/interestingness detection, classification, and clustering with empirical tests on time series/DNA/text/XML/video datasets. As a further evidence of the advantages of our method, we will demonstrate its effectiveness to solve a real world classification problem in recommending printing services and products.