AN IMPROVED ALGORITHM FOR MINING FREQUENT WEIGHTED ITEMSETS

被引：2

作者：

Nguyen Duy Ham ^{[1
]}

Bay Vo ^{[2
]}

Nguyen Thi Hong Minh ^{[3
]}

Tzung-Pei Hong ^{[4
]}

机构：

[1] Univ Peoples Secur, Dept Math & Informat, Hochiminh City, Vietnam

[2] Hochiminh City Univ Technol, Fac Informat Technol, Hochiminh City, Vietnam

[3] Ha Noi Natl Univ, Sch Grad Studies, Hanoi, Vietnam

[4] Natl Univ Kaohsiung, Dept Comp Sci & Informat Engn, Kaohsiung, Taiwan

来源：

2015 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC 2015): BIG DATA ANALYTICS FOR HUMAN-CENTRIC SYSTEMS | 2015年

关键词：

Dynamic bit vector; Frequent weighted itemset; Interval-word-segment; Multi-bit segment; Tidset;

D O I：

10.1109/SMC.2015.451

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Mining frequent weighted itemsets (FWIs) from weighted items transaction databases (WITDs) has taken the interest of many researchers and there have been several works related to mining FWIs in recent years. Beside, in real world applications, sparse weighted items transaction databases (SWITDs) are very popular. For example, in the super market there are many items, but in the transaction there is only a small number of items. This paper proposes an interval word segment (IWS) structure to store and process tidsets for enhancing effectiveness of mining FWIs from SWITDs. With this structure, intersection operations of tidsets between two itemsets are performed blazingly fast. Experimental results obtained on a number of spare databases show that IWS outperforms the existing methods.

引用

页码：2579 / 2584

页数：6

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