Voice of customer analysis using parallel association rule mining

In this paper a system for voice of customer analysis is proposed, which will produce strong rules to help organization to take business decisions. It uses parallel association rule mining for rule generation and data usually tends to be very huge so partitioning is done on the basis of sentiment of customer. For this purpose text mining algorithm is used which extracts information from unstructured data. On these partitions of data association rule mining algorithm is applied which determines strong association rules and kept in a database. Domain experts can use these rules to take business decisions which can help an organization to have a better understanding of customer's all needs and wants.

[1]  Manish Saggar,et al.  Optimization of association rule mining using improved genetic algorithms , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[2]  L. Venkata Subramaniam,et al.  Business Intelligence from Voice of Customer , 2009, 2009 IEEE 25th International Conference on Data Engineering.

[3]  Min Chen,et al.  An efficient parallel FP-Growth algorithm , 2009, 2009 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery.

[4]  Srinivasan Parthasarathy,et al.  Parallel Data Mining for Association Rules on Shared-memory Systems , 1998 .

[5]  Xie Wen-xiu,et al.  Market Basket Analysis Based on Text Segmentation and Association Rule Mining , 2010, 2010 First International Conference on Networking and Distributed Computing.

[6]  Vipin Kumar,et al.  Scalable parallel data mining for association rules , 1997, SIGMOD '97.

[7]  Masaru Kitsuregawa,et al.  Parallel mining algorithms for generalized association rules with classification hierarchy , 1997, SIGMOD '98.

[8]  Philip S. Yu,et al.  Efficient parallel data mining for association rules , 1995, CIKM '95.

[9]  Rakesh Agrawal,et al.  Parallel Mining of Association Rules , 1996, IEEE Trans. Knowl. Data Eng..

[10]  Sanguthevar Rajasekaran,et al.  A transaction mapping algorithm for frequent itemsets mining , 2006 .

[11]  Li Ning,et al.  Domain and data partitioning for parallel mining of frequent closed itemsets , 2005, ACM-SE 43.