An Efficient Decision Tree Classification Method Based on Extended Hash Table for Data Streams Mining

  • Authors:

  • Zhenzheng Ouyang;Quanyuan Wu;Tao Wang

  • Affiliations:

  • -;-;-

  • Venue:
  • FSKD '08 Proceedings of the 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 05
  • Year:
  • 2008

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Abstract

This paper focuses on continuous attributes handling for mining data stream with concept drift. Data stream is an incremental, online and real time model. Domingos and Hulten have presented a one-pass algorithm. Their system VFDT use Hoeffding inequality to achieve a probabilistic bound on the accuracy of the tree constructed. VFDT’s extended version CVFDT handles concept drift efficiently. In this paper, we revisit this problem and implemented a system HashCVFDT on top of CVFDT. It is as fast as hash table when inserting, seeking or deleting attribute value, and it also can sort the attribute value.