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
收藏
页码：88 / 89
页数：2
相关论文
共 50 条
  • [21] A genetic-fuzzy mining approach for items with multiple minimum supports
    Chen, Chun-Hao
    Hong, Tzung-Pei
    Tseng, Vincent S.
    Lee, Chang-Shing
    SOFT COMPUTING, 2009, 13 (05) : 521 - 533
  • [22] A genetic-fuzzy mining approach for items with multiple minimum supports
    Chen, Chun-Hao
    Hong, Tzung-Pei
    Tseng, Vincent S.
    Lee, Chang-Shing
    2007 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-4, 2007, : 1738 - +
  • [23] Type-2 Genetic-Fuzzy Mining with Tuning Mechanism
    Chen, Chun-Hao
    Li, Yu
    Hong, Tzung-Pei
    2015 CONFERENCE ON TECHNOLOGIES AND APPLICATIONS OF ARTIFICIAL INTELLIGENCE (TAAI), 2015, : 296 - 299
  • [24] Genetic-Fuzzy Mining with Type-2 Membership Functions
    Li, Yu
    Chen, Chun-Hao
    Hong, Tzung-Pei
    Lee, Yeong-Chyi
    2014 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE), 2014, : 1985 - 1989
  • [25] A modified approach to speed up genetic-fuzzy data mining with divide-and-conquer strategy
    Chen, Chun-Hao
    Hong, Tzung-Pei
    Tseng, Vincent S.
    2007 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-10, PROCEEDINGS, 2007, : 1 - 6
  • [26] Using GPUs to Speed Up Genetic-Fuzzy Data Mining with Evaluation on All Large Itemsets
    Chen, Chun-Hao
    Huang, Yu-Qi
    Hong, Tzung-Pei
    INTELLIGENT INFORMATION AND DATABASE SYSTEMS, ACIIDS 2022, PT I, 2022, 13757 : 17 - 26
  • [27] An improved genetic-fuzzy system for classification and data analysis
    Lahsasna, Adel
    Seng, Woo Chaw
    EXPERT SYSTEMS WITH APPLICATIONS, 2017, 83 : 49 - 62
  • [28] A Two-Stage Multi-Objective Genetic-Fuzzy Mining Algorithm
    Chen, Chun-Hao
    He, Ji-Syuan
    Hong, Tzung-Pei
    2013 IEEE INTERNATIONAL WORKSHOP ON GENETIC AND EVOLUTIONARY FUZZY SYSTEMS (GEFS), 2013, : 16 - 20
  • [29] An efficient data preprocessing method for mining customer survey data
    Zhang, N.
    Lu, W. F.
    2007 5TH IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS, VOLS 1-3, 2007, : 573 - +
  • [30] A Divide-and-Conquer Genetic-Fuzzy Mining Approach for Items with Multiple Minimum Supports
    Chen, Chun-Hao
    Hong, Tzung-Pei
    Tseng, Vincent S.
    2008 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-5, 2008, : 1233 - +
12345