A genetic-fuzzy mining approach for items with multiple minimum supports |
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Authors: | Chun-Hao Chen Tzung-Pei Hong Vincent S Tseng Chang-Shing Lee |
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Affiliation: | (1) Department of Computer Science and Information Engineering, National Cheng-Kung University, Tainan, 701, Taiwan, ROC;(2) Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, 811, Taiwan, ROC;(3) Department of Computer Science and Information Engineering, National University of Tainan, Tainan, 701, Taiwan, ROC;(4) Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, 804, Taiwan, ROC |
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Abstract: | Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific
purposes. Mining association rules from transaction data is most commonly seen among the mining techniques. Most of the previous
mining approaches set a single minimum support threshold for all the items and identify the relationships among transactions
using binary values. In the past, we proposed a genetic-fuzzy data-mining algorithm for extracting both association rules
and membership functions from quantitative transactions under a single minimum support. In real applications, different items
may have different criteria to judge their importance. In this paper, we thus propose an algorithm which combines clustering,
fuzzy and genetic concepts for extracting reasonable multiple minimum support values, membership functions and fuzzy association
rules from quantitative transactions. It first uses the k-means clustering approach to gather similar items into groups. All items in the same cluster are considered to have similar
characteristics and are assigned similar values for initializing a better population. Each chromosome is then evaluated by
the criteria of requirement satisfaction and suitability of membership functions to estimate its fitness value. Experimental
results also show the effectiveness and the efficiency of the proposed approach. |
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Keywords: | Data mining Genetic-fuzzy algorithm k-means Clustering Multiple minimum supports Requirement satisfaction |
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