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Multimedia Data Mining Using P-Trees

  • William Perrizo
  • William Jockheck
  • Amal Perera
  • Dongmei Ren
  • Weihua Wu
  • Yi Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2797)

Abstract

Peano count trees (P-trees) provide efficient, lossless, data mining ready representations of tabular data and make possible the mining of multiple very large data sets, including time-sequences of Remotely Sensed Imagery (RSI) and micro-array gene expression datasets (MA). Each MA dataset presents a one-time, gene expression level map of thousands of genes subjected to hundreds of conditions. MA data has traditionally been archived as text abstracts (e.g., Medline abstracts). An important multimedia application is to integrate macro-scale analysis of RSI with the micro-scale analysis of MA across multiple plant organisms. This is truly a multimedia data mining problem. Most multimedia data is mined by extracting pertinent features into tables, then mining the tables. P-trees are a convenient technology to mine all such multimedia data.

Keywords

Spatial – Temporal Data Mining Multimedia P-tree 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • William Perrizo
    • 1
  • William Jockheck
    • 1
  • Amal Perera
    • 1
  • Dongmei Ren
    • 1
  • Weihua Wu
    • 1
  • Yi Zhang
    • 1
  1. 1.Department of Computer ScienceNorth Dakota State UniversityFargo

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