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  An Implementation of New Frequent Pattern Mining Algorithm for Business Intelligence Solution  
  Authors : Ujwala Mhashakhetri; Dr. Rahila Sheikh
  Cite as:

 

In the digital world, frequent pattern mining algorithm is used widely for business implementation in the areas like online marketing, sales and advertisement. In order to make better business decision both on the individual and organizational level we search for the others opinion. Social media, discussion forums, review, blogs and micro-blogs are opinion rich resources. Mined information from these resources can be successfully utilized for decision making by customer or organization if opinion orientation are considered carefully. In this paper, we propose a business intelligence solution using frequent pattern mining. Evaluation results show that the proposed algorithm is more accurate, efficient and work better with dense datasets.

 

Published In : IJCSN Journal Volume 5, Issue 5

Date of Publication : October 2016

Pages : 828-835

Figures :16

Tables : --

 

Ujwala Mhashakhetri : Department of Computer Science & Engineering Rajiv Gandhi College of Engineering, Research and Technology Chandrapur, Maharashtra, India

Dr. Rahila Sheikh : Department of Computer Science & Engineering Rajiv Gandhi College of Engineering, Research and Technology Chandrapur, Maharashtra, India

 

 

 

 

 

 

 

Data Mining, Frequent Pattern Mining, Frequent, Opinion Mining

In this paper, we have proposed the new algorithm with itemset tree data structure for determining product popularity in the market from the incremental customer review datasets. Latter the algorithm is implemented on review datasets and performance is calculated. Result shows that it works better than Naïve bayes algorithm. Again the result of comparison shows that proposed algorithm works better than H-mine algorithm in dense datasets while H-mine algorithm works better in sparse datasets. As mining progresses the projected database goes smaller making it denser.

 

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