Wavelet density estimators over data streams

  • Authors:
  • Christoph Heinz;Bernhard Seeger

  • Affiliations:
  • Philipps University Marburg;Philipps University Marburg

  • Venue:
  • Proceedings of the 2005 ACM symposium on Applied computing
  • Year:
  • 2005

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Abstract

Density estimation is a building block of many data analysis techniques. A recently examined approach based on wavelets promises to be superior to traditional density estimation techniques. For possibly infinite data streams, however, this approach is not feasible due to the limited resources, e.g. memory. In this paper, we propose a new technique for computing wavelet density estimators over data streams that only requires a fixed amount of memory. Our estimators are updated in an online manner such that a continuous analysis of data streams is supported during runtime.