Mining Indirect Association Rules

One of the interesting and meaningful information that is hiding in transactional database is indirect association rule. It corresponds to the property of high dependencies between two items that are rarely occurred together but indirectly emerged via another items. Since indirect association rule is nontrivial information, it can implicitly give a new perspective of relationship which cannot be directly observed from the common rule. Therefore, we proposed an algorithm for Mining Indirect Least Association Rule (MILAR) from the real and benchmarked datasets. MILAR is embedded with our scalable least measure namely Critical Relative Support (CRS). The experimental results show that MILAR can generate the desired rules in term of least and indirect least association rules. In addition, the obtained results can also be used by the domain experts to do further analysis and finally reveal more interesting findings

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Mining Indirect Association Rules for Web Recommendation

International Journal of Applied Mathematics and Computer Science ◽

10.2478/v10006-009-0015-5 ◽

2009 ◽

Vol 19 (1) ◽

pp. 165-186 ◽

Cited By ~ 22

Author(s):

Przemysław Kazienko

Keyword(s):

Association Rules ◽

Main Idea ◽

Web Pages ◽

New Approach ◽

Important Measure ◽

New Knowledge ◽

Direct Association ◽

Indirect Association ◽

Classical Association ◽

The Web

Mining Indirect Association Rules for Web RecommendationClassical association rules, here called "direct", reflect relationships existing between items that relatively often co-occur in common transactions. In the web domain, items correspond to pages and transactions to user sessions. The main idea of the new approach presented is to discover indirect associations existing between pages that rarely occur together but there are other, "third" pages, called transitive, with which they appear relatively frequently. Two types of indirect associations rules are described in the paper: partial indirect associations and complete ones. The former respect single transitive pages, while the latter cover all existing transitive pages. The presented IDARM* Algorithm extracts complete indirect association rules with their important measure—confidence—using pre-calculated direct rules. Both direct and indirect rules are joined into one set of complex association rules, which may be used for the recommendation of web pages. Performed experiments revealed the usefulness of indirect rules for the extension of a typical recommendation list. They also deliver new knowledge not available to direct ones. The relation between ranking lists created on the basis of direct association rules as well as hyperlinks existing on web pages is also examined.

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