Improving Business Intelligence Based on Frequent Itemsets Using k-Means Clustering Algorithm

  • Prabhu PaulrajEmail author
  • Anbazhagan Neelamegam
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 284)


In this world, each and every activity is enriched with lot of information. Business and other organization needs information for better decision making. Business Intelligence is a set of methods, process and technologies that transform raw data into meaningful and useful information. Some of the functions of business intelligence technologies are reporting, Online Analytical Processing, Online Transaction processing, data mining, process mining, complex event processing, business performance management, benchmarking and text mining. The applications of business intelligence includes E-commerce recommender system, approval of bank loan, credit/debit card fraud detection etc., In order to obtain business intelligence from large dataset there many techniques are available in data mining such as characterization, discrimination, frequent itemset mining, outlier analysis, cluster analysis and so on. In this proposed algorithm frequent itemset mining and clustering algorithm is used to extract the information from the dataset in order to make the decision making process more efficient and to improve the business intelligence.


Support Vector Machine Recommender System Frequent Itemsets Collaborative Filter Frequent Item 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Information Technology, DDEAlagappa UniversityKaraikudiIndia
  2. 2.Department of MathematicsAlagappa UniversityKaraikudiIndia

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