Filtering association rules by their semantics and structures
Association rule mining produces a large number of rules but many of them are usually redundant ones. When a data set contains infrequent items, the authors need to set the minimum support criterion very low; otherwise, these items will not be discovered. The downside is that it leads to even more r...
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| Format: | Chapter |
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2018
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| Online Access: | https://repository.li.mahidol.ac.th/handle/123456789/28352 |
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| Institution: | Mahidol University |
| Summary: | Association rule mining produces a large number of rules but many of them are usually redundant ones. When a data set contains infrequent items, the authors need to set the minimum support criterion very low; otherwise, these items will not be discovered. The downside is that it leads to even more redundancy. To deal with this dilemma, some proposed more efficient, and perhaps more complicated, rule generation methods. The others suggested using simple rule generation methods and rather focused on the post-pruning of the rules. This chapter follows the latter approach. The classic Apriori is employed for the rule generation. Their goal is to gain as much insight as possible about the domain. Therefore, the discovered rules are filtered by their semantics and structures. An individual rule is classified by its own semantic, or by how clear its domain description is. It can be labelled as one of the following: strongly meaningless, weakly meaningless, partially meaningful, and meaningful. In addition, multiple rules are compared. Rules with repetitive patterns are removed, while those conveying the most complete information are retained. They demonstrate an application of our techniques to a real case study, an analysis of traffic accidents in Nakorn Pathom, Thailand. © 2010, IGI Global. |
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