Mining fuzzy association rules in data streams

Author

Chen, Peng ; Su, Hongye ; Guo, Lichao ; Qu, Yu

Author_Institution

State Key Lab. of Ind. Control Technol., Zhejiang Univ., Hangzhou, China

Volume

4

fYear

2010

fDate

16-18 April 2010

Abstract

Many algorithms have been proposed for mining fuzzy association rules in static datasets with quantitative attributes. However, there is few study on mining fuzzy association rules in data streams. This paper presents an algorithm FFI_Stream for fuzzy association rules mining in data streams. Efficient techniques are presented to find fuzzy association rules in data streams using time based sliding window model. The clustering technique is used to find the fuzzy sets. Membership Function Bias measure (MFB_measure) is introduced to evaluate the membership function in each sliding window. Selectively Updating Mechanism (SUM) and Projected Summaries (PS) are proposed to update the fuzzy sets dynamically. Then, experiments are carried out on both synthetic and real life datasets. The results show that the algorithm is effective and efficient.

Keywords

data mining; fuzzy set theory; pattern clustering; FFI_Stream algorithm; clustering technique; data stream; fuzzy association rule mining; fuzzy set; membership function bias measure; projected summaries; quantitative attribute; real life dataset; selectively updating mechanism; static dataset; synthetic dataset; time based sliding window model; Association rules; Change detection algorithms; Clustering algorithms; Data mining; Fuzzy control; Fuzzy sets; Industrial control; Itemsets; Monitoring; Partitioning algorithms; Fuzzy association rules mining; change detection; data stream; membership function; sliding window;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Engineering and Technology (ICCET), 2010 2nd International Conference on

Conference_Location

Chengdu

Print_ISBN

978-1-4244-6347-3

Type

conf

DOI

10.1109/ICCET.2010.5485665

Filename

5485665