Enhancing the Efficiency in Mining Weighted Frequent Itemsets

Author

Guo-Cheng Lan ; Tzung-Pei Hong ; Hong Yu Lee ; Shyue-liang Wang ; Chun-Wei Tsai

Author_Institution

Dept. of Comput. Sci. & Inf. Eng., Nat. Cheng Kung Univ., Tainan, Taiwan

fYear

2013

fDate

13-16 Oct. 2013

Firstpage

1104

Lastpage

1108

Abstract

To further enhance the performance of finding weighted frequent item sets, this work presents an effective upper-bound model for reducing unpromising candidates in mining process. To achieve this goal, a projection-based pruning strategy based on our previously proposed model is developed to gradually tighten the upper-bound value for each transaction. The experimental results show that the proposed approach can achieve good performance in efficiency.

Keywords

data mining; data mining process; projection-based pruning strategy; upper-bound model; weighted frequent itemsets; Algorithm design and analysis; Conferences; Data mining; Educational institutions; Itemsets; Data mining; upper-bound model; weighted data mining; weighted frequent itemset mining;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on

Conference_Location

Manchester

Type

conf

DOI

10.1109/SMC.2013.192

Filename

6721945

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3038765