AN EFFICIENT DATA MINING METHOD TO FIND FREQUENT ITEM SETS IN LARGE DATABASE USING TR- FCTM

Mining association rules in large database is one of most popular data mining techniques for business decision makers. Discovering frequent item set is the core process in association rule mining. Numerous algorithms are available in the literature to find frequent patterns. Apriori and FP-tree are the most common methods for finding frequent items. Apriori finds significant frequent items using candidate generation with more number of data base scans. FP-tree uses two database scans to find significant frequent items without using candidate generation. This proposed TR-FCTM (Transaction Reduction- Frequency Count Table Method) discovers significant frequent items by generating full candidates once to form frequency count table with one database scan. Experimental results of TR-FCTM shows that this algorithm outperforms than Apriori and FP-tree.

[1]  Shyam S Karanth,et al.  Improved Aprori Algorithm Based on bottom up approach using Probability and Matrix , 2012 .

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

[3]  Mohammed J. Zaki,et al.  CHARM: An Efficient Algorithm for Closed Association Rule Mining , 2007 .

[4]  Nirav Bhatt,et al.  Data Mining Techniques and Trends – A Review , 2016 .

[5]  Philip S. Yu,et al.  Using a Hash-Based Method with Transaction Trimming and Database Scan Reduction for Mining Associati , 1997 .

[6]  Philip S. Yu,et al.  Using a Hash-Based Method with Transaction Trimming for Mining Association Rules , 1997, IEEE Trans. Knowl. Data Eng..

[7]  Vincent S. Tseng,et al.  EFIM: A Highly Efficient Algorithm for High-Utility Itemset Mining , 2015, MICAI.

[8]  Ada Wai-Chee Fu,et al.  Mining frequent itemsets without support threshold: with and without item constraints , 2004, IEEE Transactions on Knowledge and Data Engineering.

[9]  Jian Pei,et al.  Mining frequent patterns without candidate generation , 2000, SIGMOD '00.

[10]  T Purusothaman,et al.  UTILITY SENTIENT FREQUENT ITEM SET MINING AND ASSOCIATION RULE MINING: A LITERATURE SURVEY AND COMPARATIVE STUDY , 2009 .

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

[12]  Yen-Liang Chen,et al.  Mining association rules with multiple minimum supports: a new mining algorithm and a support tuning mechanism , 2004, Decision Support Systems.

[13]  Huali Liu,et al.  An Improved Apriori Algorithm for Association Rules , 2013 .

[14]  Hannu Toivonen,et al.  Sampling Large Databases for Association Rules , 1996, VLDB.

[15]  Gopal K Gupta,et al.  Introduction to Data Mining with Case Studies , 2011 .

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

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

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

[19]  Saravanan Suba,et al.  A Study on Milestones of Association Rule Mining Algorithms in Large Databases , 2012 .