An attempt of hybridization of generalized dynamic reducts and a heuristic attribute reduction using reduced decision tables

Author

Kudo, Yasuo ; Murai, Takashi

Author_Institution

Coll. of Inf. & Syst, Muroran Inst. of Technol., Muroran, Japan

fYear

2013

fDate

7-10 July 2013

Firstpage

1

Lastpage

6

Abstract

Mining from big data is one of the hottest topics in current trends of computer science. Attribute reduction to compute relative reducts from a given dataset is a key technique to use rough set theory as a tool in data mining, however, attribute reduction from a given dataset with numerous objects and attributes is very difficult. In this paper, to achieve attribute reduction from data with numerous objects and attributes, we try to combine generalized dynamic reducts and a heuristic attributed reduction using reduced decision tables.

Keywords

computer science; data mining; decision tables; rough set theory; computer science; data mining; generalized dynamic reducts; heuristic attribute reduction; hybridization; reduced decision tables; rough set theory; Approximation methods; Data handling; Data mining; Data storage systems; Heuristic algorithms; Information management; Set theory; attribute reduction; generalized dynamic reduct; reduced decision table; rough set;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems (FUZZ), 2013 IEEE International Conference on

Conference_Location

Hyderabad

ISSN

1098-7584

Print_ISBN

978-1-4799-0020-6

Type

conf

DOI

10.1109/FUZZ-IEEE.2013.6622537

Filename

6622537