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

Author

Ouyang, Zhenzheng ; Wu, Quanyuan ; Wang, Tao

Author_Institution

Zhenzheng Ouyang Sci. Sch., Nat. Univ. of Defense Technol., Changsha

Volume

5

fYear

2008

fDate

18-20 Oct. 2008

Firstpage

313

Lastpage

317

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. VFDTpsilas 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.

Keywords

data mining; decision trees; file organisation; pattern classification; Hoeffding inequality; continuous attributes handling; data streams mining; decision tree classification; extended hash table; Classification tree analysis; Data mining; Data processing; Decision trees; Fuzzy systems; Memory management; Sorting; Statistics; Testing; Time factors; Continuous Attribute; Data Streams; Extended Hash Table;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems and Knowledge Discovery, 2008. FSKD '08. Fifth International Conference on

Conference_Location

Jinan Shandong

Print_ISBN

978-0-7695-3305-6

Type

conf

DOI

10.1109/FSKD.2008.481

Filename

4666543