Efficient Data Preprocessing for Genetic-Fuzzy Mining with MapReduce

被引：0

作者：

Hong, Tzung-Pei ^{[1
]}

Liu, Yu-Yang ^{[2
]}

Wu, Min-Thai ^{[1
]}

Tsai, Chun-Wei

机构：

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

[2] Natl Sun Yat Sen Univ, Dept Comp Sci & Engn, Kaohsiung 80424, Taiwan

来源：

2015 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS - TAIWAN (ICCE-TW) | 2015年

关键词：

RULES;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Genetic-fuzzy data mining can successfully find out linguistic association rules and appropriate membership functions close to human concepts from quantitative transactions, and thus becomes a promising research field in these years. It repeatedly uses fuzzy frequent 1-itemsets to evaluate fitness values of chromosomes, which is very time-consuming. In this paper, we propose a MapReduce preprocessing approach to efficiently transform given quantitative transaction data into pairs of items and quantity lists to increase the performance of genetic-fuzzy mining. The MapReduce architecture totally fits the conversion due to its characteristics of key-value format. Experimental results also show the effect of the proposed approach.

引用

页码：88 / 89

页数：2

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