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- W1570372311 abstract "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." @default.
- W1570372311 created "2016-06-24" @default.
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- W1570372311 date "2015-06-01" @default.
- W1570372311 modified "2023-09-22" @default.
- W1570372311 title "Efficient data preprocessing for genetic-fuzzy mining with MapReduce" @default.
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- W1570372311 doi "https://doi.org/10.1109/icce-tw.2015.7217045" @default.
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