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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Pratima Gautam ¹, K. R. Pardasani ²

Affiliations
1 Manit, Bhopal, IN
2 Department of Mathematics & Computer Application, Manit, Bhopal, IN

Abstract

We have extended the capacity of the learn of mining association rules from single level to multiple concept levels and studied methods for mining multiple-level association rules from large transaction databases. Mining multiple-level association rules may lead to progressive mining of refined knowledge from data and have interesting applications for knowledge discovery in transaction databases, as well as other business or engineering databases. Mining frequent patterns in huge transactional database is an extremely researched area in the field of data mining. Mining frequent itemsets is a basic problem for mining association rules. Taking out association rules at multiple levels helps in discovers more specific and applicable knowledge. Even as computing the number of occurrence of an item we require to scan the given database lots of times. Thus we used partition method and boolean methods for finding frequent itemsets at each concept levels which reduce the number of scans, I/O cost and also reduce CPU overhead. In this paper a new approach is introduced for solving the above mentioned issues. Therefore this algorithm is above all fit for very large size databases. We also use a top-down progressive deepening method is developed for efficient mining of multiple-level association rules from large transaction databases based on the Apriori principle. This method first finds frequent data items at the topmost level and then progressively deepens the mining process into their descendants at lower concept levels.

Keywords

Association Rule, Frequent Itemset, Transaction Database, Tree Map, Multilevel Association Rule, Level Wise Filtered Tables.

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Pratima Gautam ¹, K. R. Pardasani ²

Affiliations
1 Maulana Azad National Institute of Technology, Bhopal, IN
2 Department of Mathematics and Computer Application in Maulana Azad National Institute of Technology, Bhopal, IN

Abstract

Data mining is having a vital role in many of the applications like market-basket analysis, in biotechnology field etc. In data mining, frequent itemsets plays an important role which is used to identify the correlations among the fields of database. The problem of developing models and algorithms for multilevel association mining pose for new challenges for mathematics and computer science. In most of the studies, multilevel rules will be mined through repeated mining from databases or mining the rules at each individually levels, it affects the efficiency, integrality and accuracy. This paper proposes a hash based method for multilevel association rule mining, which extracting knowledge implicit in transactions database with different support at each level. The proposed algorithm adopts a top-down progressively deepening approach to derive large itemsets. This approach incorporates boundaries instead of sharp boundary intervals. An example is also given to demonstrate that the proposed mining algorithm can derive the multiple-level association rules under different supports in a simple and effective manner.

Keywords

Association Rule, Multilevel, Frequent Items, Transactional Database.

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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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