| Title: | GS3-6 Multi Objective Evolutionary Algorithms for Association Rule Mining: Advances and Challenges |
|---|---|
| Publication: | ICAROB2016 |
| Volume: | 21 |
| Pages: | 467-476 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2016.GS3-6 |
| Author(s): | Aswini Kumar Patra |
| Publication Date: | January 29, 2016 |
| Keywords: | Association Rule Mining, Categorical, Quantitative and Fuzzy Association Rules, MOEAs |
| Abstract: | Association rule mining is an important research area in data mining field. The challenge, posed by many methods, is the amount of time consumed for generating frequent items sets. To overcome this, evolutionary algorithms have been used widely. Moreover, apart from support and confidence, there are many other metrics available to measure the quality of association rules. That is the reason why multi-objective approach plays a crucial role. Therefore, two methodologies namely, multi-objective and evolutionary algorithms as a combination proved to be a preferred choice. Though numerous works have been proposed for mining association rules, use of multi-objective evolutionary algorithms are still in its infancy stage. This paper explores the challenges and advances that have been made in this regard in terms of nature of algorithm, encoding mechanism, objective functions and operators. |
| PDF File: | https://alife-robotics.co.jp/members2016/icarob/data/papers/GS/GS3-6.pdf |
| Copyright: | © The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/ |
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