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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

	ASCII Text	x
	Chris Giannella, Kun Liu, Todd Olsen, Hillol Kargupta, "Communication Efficient Construction of Decision Trees Over Heterogeneously Distributed Data," Data Mining, IEEE International Conference on, pp. 67-74, Fourth IEEE International Conference on Data Mining (ICDM'04), 2004.

	BibTex	x
	@article{ 10.1109/ICDM.2004.10114, author = {Chris Giannella and Kun Liu and Todd Olsen and Hillol Kargupta}, title = {Communication Efficient Construction of Decision Trees Over Heterogeneously Distributed Data}, journal ={Data Mining, IEEE International Conference on}, volume = {0}, year = {2004}, isbn = {0-7695-2142-8}, pages = {67-74}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2004.10114}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Data Mining, IEEE International Conference on TI - Communication Efficient Construction of Decision Trees Over Heterogeneously Distributed Data SN - 0-7695-2142-8 SP67 EP74 A1 - Chris Giannella, A1 - Kun Liu, A1 - Todd Olsen, A1 - Hillol Kargupta, PY - 2004 KW - Decision Trees KW - Distributed Data Mining KW - Random Projection VL - 0 JA - Data Mining, IEEE International Conference on ER -

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

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2004.10114

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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