Data Mining and Knowledge Engineering

Vikrant Sabnis ¹, Neelu Khare ¹, R. S. Thakur ¹, K. R. Pardasani ²

Affiliations
1 Department of Computer Applications, Maulana Azad National Institute of Technology, Bhopal, M.P, IN
2 Department of Mathematics, Maulana Azad National Institute of Technology, Bhopal, M.P, IN

Abstract

Web content mining refers to description and discovery of useful information from the web contents/data/documents. Hypertext is one of the most common web content data that has hyperlinks in addition to text. These are modeled with multiple levels of details depending on the application. In this paper Karnaugh map model for multilevel association rule mining has been developed to investigate association relationships among hypertexts of a web site. Karnaugh map model needs single scan of data and stores the information in the form of frequency. Model adopts progressively deepening approach for finding large text sets by utilizing karnaugh map logic for finding frequent text sets at each level of abstraction. Frequent texts sets are generated by the karnaugh map model are used to discover strong association relationships among hypertexts at different levels of abstraction. Further the rules are categorized under three categories and their behavior is studied across the level of abstractions.

Keywords

Karnaugh Map Model, Multilevel Association Rules, Association Relationships, Frequent Text Set.

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Neelu Khare ¹, Neeru Adlakha ², K. R. Pardasani ³

Affiliations
1 Department of Computer Applications, Maulana Azad National Institute of Technology, Bhopal (M.P), IN
2 Department of Applied Mathematics & Humanities, SVNIT, Surat, IN
3 Department of Mathematics, Maulana Azad National Institute of Technology, Bhopal (M.P), IN

Abstract

Mining association rules in transactional or relational databases is an important task in data mining. Fuzzy predicates have been incorporated into association rule mining to extend types of data relationships that can be represented, for interpretation of rules in linguistic terms and to avoid fix boundaries in partitioning data attributes. In this paper, the mining of single dimensional association rule and non-repetitive predicate multi-dimensional association rule are combined over the transactions of multidimensional transaction database. The algorithm mines conditional hybrid dimension association rules which satisfy the definite condition on the basis of multi-dimensional transaction database. In this algorithm each predicate should be partitioned at the fuzzy set level, the support count of itemsets is calculated by performing fuzzy AND operation on items that constitute the itemsets. Apriori property is used in algorithm to prune the item sets. The implementation of algorithm is illustrated with the help of a simple example.

Keywords

Fuzzy Set Level, Hybrid Dimensional Association Rule, Multi-Dimensional Transaction Database, Conditional Restrict, Predicate.

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