A novel algorithm for optimization of association rule with Karnagh map and genetic algorithm

Association Rule mining is a very efficient technique for finding correlation among data sets. The correlation of data gives meaning full extraction process. For the mining of rules varieties of algorithms are used such as Apriori algorithm and Tree based algorithm. Some algorithm is wonder performance but generates redundant association rule and also suffered from multiple scanning problem. In this paper we proposed an algorithm for association rule mining based on Karnaugh Map and Genetic Algorithm. In this approach, support count can be calculated directly from K-Map, so no further scanning of the database is required. With genetic algorithm, a global search can be performed and found an optimal rule. We experimentally evaluate our approach, and demonstrate that our algorithm significantly reduces the computational costs and generate an optimal association rule only. For evaluating algorithm conducted the real world dataset “The National Rural Employment Guarantee Act” (NREGA) Department of Rural Development Government of India.

[1]  Shamkant B. Navathe,et al.  An Efficient Algorithm for Mining Association Rules in Large Databases , 1995, VLDB.

[2]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[3]  Rajeev Motwani,et al.  Dynamic itemset counting and implication rules for market basket data , 1997, SIGMOD '97.

[4]  Roliana Ibrahim,et al.  Association Rule Mining through Matrix Manipulation using Transaction Patternbase , 2012 .

[5]  Das Amrita,et al.  Mining Association Rules between Sets of Items in Large Databases , 2013 .

[6]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[7]  Zhigang Lu,et al.  An Improved Apriori-based Algorithm for Association Rules Mining , 2009, 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery.

[8]  Zvi M. Kedem,et al.  Pincer-Search: A New Algorithm for Discovering the Maximum Frequent Set , 1998, EDBT.

[9]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[10]  Damla Oğuz.  Dynamic frequent itemset mining based on Matrix Appriori algorithm , 2012 .

[11]  Wee Keong Ng,et al.  Rapid association rule mining , 2001, CIKM '01.

[12]  Kalyanmoy Deb,et al.  An introduction to genetic algorithms , 1999 .

[13]  Jian Pei,et al.  Mining frequent patterns by pattern-growth: methodology and implications , 2000, SKDD.

[14]  Philip S. Yu,et al.  An effective hash-based algorithm for mining association rules , 1995, SIGMOD '95.

[15]  Vishal Sharma,et al.  Distance Weight Parity Optimization of Association Rule Mining With Genetic Algorithm , 2012 .

[16]  Ramakrishnan Srikant,et al.  Mining generalized association rules , 1995, Future Gener. Comput. Syst..

[17]  Venansius Baryamureeba,et al.  Optimized Association Rule Mining with genetic algorithms , 2011, 2011 Seventh International Conference on Natural Computation.

[18]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.

[19]  Kirat Pal Singh,et al.  Design of High Performance MIPS Cryptography Processor Based on T-DES Algorithm , 2015, ArXiv.