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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