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Fourth IEEE International Conference on Data Mining (ICDM'04)
Communication Efficient Construction of Decision Trees Over Heterogeneously Distributed Data
Brighton, United Kingdom
November 01-November 04
ISBN: 0-7695-2142-8
Chris Giannella, University of Maryland Baltimore County, Baltimore, MD
Kun Liu, University of Maryland Baltimore County, Baltimore, MD
Todd Olsen, University of Maryland Baltimore County, Baltimore, MD
Hillol Kargupta, University of Maryland Baltimore County, Baltimore, MD
We present an algorithm designed to efficiently construct a decision tree over heterogeneously distributed data without centralizing. We compare our algorithm against a standard centralized decision tree implementation in terms of accuracy as well as the communication complexity. Our experimental results show that by using only 20% of the communication cost necessary to centralize the data we can achieve trees with accuracy at least 80% of the trees produced by the centralized version.
Index Terms:
Decision Trees, Distributed Data Mining, Random Projection
Citation:
Chris Giannella, Kun Liu, Todd Olsen, Hillol Kargupta, "Communication Efficient Construction of Decision Trees Over Heterogeneously Distributed Data," icdm, pp.67-74, Fourth IEEE International Conference on Data Mining (ICDM'04), 2004
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