A multi-level ant-colony mining algorithm for membership functions

  • Authors:

  • Tzung-Pei Hong;Ya-Fang Tung;Shyue-Liang Wang;Yu-Lung Wu;Min-Thai Wu

  • Affiliations:

  • Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung 811, Taiwan and Department of Computer Science and Engineering, National Sun Yat-sen Univers ...;Institute of Information Management, I-Shou University, Kaohsiung 840, Taiwan;Department of Information Management, National University of Kaohsiung, Kaohsiung 811, Taiwan;Institute of Information Management, I-Shou University, Kaohsiung 840, Taiwan;Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung 804, Taiwan

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2012

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Abstract

Fuzzy data mining is used to extract fuzzy knowledge from linguistic or quantitative data. It is an extension of traditional data mining and the derived knowledge is relatively meaningful to human beings. In the past, we proposed a mining algorithm to find suitable membership functions for fuzzy association rules based on ant colony systems. In that approach, precision was limited by the use of binary bits to encode the membership functions. This paper elaborates on the original approach to increase the accuracy of results by adding multi-level processing. A multi-level ant colony framework is thus designed and an algorithm based on the structure is proposed to achieve the purpose. The proposed approach first transforms the fuzzy mining problem into a multi-stage graph, with each route representing a possible set of membership functions. The new approach then extends the previous one, using multi-level processing to solve the problem in which the maximum quantities of item values in the transactions may be large. The membership functions derived in a given level will be refined in the subsequent level. The final membership functions in the last level are then outputted to the rule-mining phase to find fuzzy association rules. Experiments are also performed to show the performance of the proposed approach. The experimental results show that the proposed multi-level ant colony systems mining approach can obtain improved results.