论文信息 - A NEW HYBRID ALGORITHM FOR BUSINESS INTELLIGENCE RECOMMENDER SYSTEM

A NEW HYBRID ALGORITHM FOR BUSINESS INTELLIGENCE RECOMMENDER SYSTEM

Business Intelligence is a set of methods, process and technologies that transform raw data into meaningful and useful information. Recommender system is one of business intelligence system that is used to obtain knowledge to the active user for better decision making. Recommender systems apply data mining techniques to the problem of making personalized recommendations for information. Due to the growth in the number of information and the users in recent years offers challenges in recommender systems. Collaborative, content, demographic and knowledge-based are four different types of recommendations systems. In this paper, a new hybrid algorithm is proposed for recommender system which combines knowledge based, profile of the users and most frequent item mining technique to obtain intelligence.

N. Anbazhagan | P. Prabhu

[1] David M. Rocke,et al. Sampling and Subsampling for Cluster Analysis in Data Mining: With Applications to Sky Survey Data , 2003, Data Mining and Knowledge Discovery.

[2] M. H. Marghny,et al. An Effective Evolutionary Clustering Algorithm: Hepatitis C Case Study , 2011, ArXiv.

[3] Xindong Wu,et al. Support vector machines based on K-means clustering for real-time business intelligence systems , 2005, Int. J. Bus. Intell. Data Min..

[4] Ravindra Jain,et al. A hybrid clustering algorithm for data mining , 2012, ArXiv.

[5] John Riedl,et al. Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[6] Bingsheng He,et al. Frequent itemset mining on graphics processors , 2009, DaMoN '09.

[7] Alexandre V. Evfimievski,et al. Privacy preserving mining of association rules , 2002, Inf. Syst..

[8] Risto Vaarandi,et al. A data clustering algorithm for mining patterns from event logs , 2003, Proceedings of the 3rd IEEE Workshop on IP Operations & Management (IPOM 2003) (IEEE Cat. No.03EX764).

[9] P. Prabhu. Improving the Performance of K-Means Clustering for High Dimensional Dataset , 2011 .

[10] Hsinchun Chen,et al. A comparison of collaborative-filtering algorithms for ecommerce , 2007 .