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Gautam, Pratima
- A Partition Model for Multilevel Association Rule Mining
Authors
1 Manit, Bhopal, IN
2 Department of Mathematics & Computer Application, Manit, Bhopal, IN
Source
Data Mining and Knowledge Engineering, Vol 3, No 8 (2011), Pagination: 507-515Abstract
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.
- A Fast Algorithm for Multilevel Association Rule Using Hash Based Method
Authors
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
Source
Data Mining and Knowledge Engineering, Vol 2, No 7 (2010), Pagination: 123-129Abstract
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.