Volume & Issue no: Volume 4, Issue 4, July - August 2015
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Title: |
Combined Mining Approach to Generate Informative Patterns |
Author Name: |
Priyanka Wani Kapadia |
Abstract: |
Abstract
Business data mining applications involve huge amount of
heterogeneous, distributed data. In such a case to use
traditional data mining algorithms for obtaining
comprehensive information about business, which will be
helpful for decision making, is very time and space
consuming. Traditional data mining methods involve single
step data mining process to generate patterns and also they
deal with homogeneous features of dataset. They need to
follow join operation to get useful information from multiple
large data sources. We consider Combined Mining as an
approach to generate more informative patterns by
considering multiple data sources or multiple features or
multiple methods. Here we are going to discuss multifeature
combined mining and multimethod combined mining
methods. In multifeature combined mining, we obtained pair
patterns, incremental pair patterns and cluster patterns by
considering multiple heterogeneous features from data
sources. In multimethod combined mining approach, multiple
data mining methods has been used to generate more
informative knowledge.
Keywords: Actionable knowledge discovery, association
rule mining, combined mining, data mining, FP-Growth,
interestingness metrics |
Cite this article: |
Priyanka Wani Kapadia , "
Combined Mining Approach to Generate Informative Patterns" , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) ,
Volume 4, Issue 4, July - August 2015 , pp.
103-109 , ISSN 2278-6856.
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