Performance Comparison of Frequent Pattern Mining Algorithms for Business Intelligence Analytics

Objectives: In this paper, a simple and flexible partition algorithm has been proposed to mine frequent data item sets. This partition algorithm is different from other frequent pattern mining algorithm like Apriori algorithm, AprioriAllHybrid algorithm etc. Method: Partition algorithm concept has been proposed to increase the execution speed with minimum cost. Initially only for one time the database is scanned and separate partitions will be created for each sets of itemsets, which is 1-itemset, 2-itemsets, 3-itemsets etc. Findings: The scanning of whole database is not necessary to get the count of an itemset, it is enough to get the count of each data itemsets from its partition. This partition algorithm approach is implemented and evaluated against AprioriAllHybrid and Apriori algorithm. The candidate itemsets generated at each step is reduced and the scanning time is also reduced. The proposed methodology performance is significantly better than other algorithms and it promotes the faster execution time for mining frequent patterns. Applications: This proposed algorithm is used in areas like retail sales, production, universities, finance, banking systems and for business to plan and estimate the future values.

[1]  Adeel Shiraz Hashmi,et al.  Big Data Mining Techniques , 2016 .

[2]  Ait-Mlouk Addi,et al.  Comparative survey of association rule mining algorithms based on multiple-criteria decision analysis approach , 2015, 2015 3rd International Conference on Control, Engineering & Information Technology (CEIT).

[3]  Zhuo Wang,et al.  Rough_Apriori Algorithm and the Application of an Aid System of Campus Student Major Selection , 2009, 2009 International Conference on Research Challenges in Computer Science.

[4]  Haibin Zhu,et al.  An Algorithm to Improve the Effectiveness of Apriori , 2007, 6th IEEE International Conference on Cognitive Informatics.

[5]  Fan Guidan,et al.  A Frequent Itemsets Mining Algorithm Based on Matrix in Sliding Window over Data Streams , 2013, 2013 Third International Conference on Intelligent System Design and Engineering Applications.

[6]  Cao Xiaojun.  Mining Accurate Top-K Frequent Closed Itemset from Data Stream , 2012, 2012 International Conference on Computer Science and Electronics Engineering.

[7]  Chamran Asgari,et al.  Provide A New Approach for Mining Fuzzy Association Rules using Apriori Algorithm , 2015 .

[8]  Nanda Kumar,et al.  Fast Algorithms for Discovering Sequential Patterns in Massive Datasets , 2011 .

[9]  Sen Zhang,et al.  New Techniques for Mining Frequent Patterns in Unordered Trees , 2015, IEEE Transactions on Cybernetics.

[10]  M. Ramaiya,et al.  Mining Positive and Negative Association Rules from Frequent and Infrequent Pattern Using Improved Genetic Algorithm , 2013, 2013 5th International Conference on Computational Intelligence and Communication Networks.

[11]  Ranjana Sikarwar,et al.  Web usage mining using improved Frequent Pattern Tree algorithms , 2014, 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT).