A Fourier Spectrum-Based Approach to Represent Decision Trees for Mining Data Streams in Mobile Environments

Hillol Kargupta; Byung Hoon Park

doi:10.1109/TKDE.2004.1269599

A Fourier Spectrum-Based Approach to Represent Decision Trees for Mining Data Streams in Mobile Environments

Hillol Kargupta
, Byung Hoon Park

Performance Engineering

Research output: Contribution to journal › Article › peer-review

36 Scopus citations

Abstract

This paper presents a novel Fourier analysis-based approach to combine, transmit, and visualize decision trees in a mobile environment. Fourier representation of a decision tree has several interesting properties that are particularly useful for mining data streams from small mobile computing devices connected through limited-bandwidth wireless networks. This paper presents algorithms to compute the Fourier spectrum of a decision tree and outlines a technique to construct a decision tree from its Fourier spectrum. It offers a framework to aggregate decision trees in their Fourier representations. It also describes the MobiMine, a mobile data stream mining system, that uses the developed techniques for mining stock-market data from handheld devices.

Original language	English
Pages (from-to)	216-229
Number of pages	14
Journal	IEEE Transactions on Knowledge and Data Engineering
Volume	16
Issue number	2
DOIs	https://doi.org/10.1109/TKDE.2004.1269599
State	Published - Feb 2004

Funding

The authors acknowledge support from the US National Science Foundation CAREER award IIS-0093353, NASA (NRA) NAS2-37143, and TEDCO. The authors would also like to thank Sweta Pittie, Patrick Blair, and Selina Liu.

Keywords

Decision trees
Fourier spectrum
Mobile data mining

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10.1109/TKDE.2004.1269599

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A Fourier Spectrum-Based Approach to Represent Decision Trees for Mining Data Streams in Mobile Environments

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