Paper Info
Title
Novel Data Segmentation Techniques for Efficient Discovery of Correlated Patterns Using Parallel Algorithms.
Abstract
Efficient discovery of interesting patterns using parallel algorithms is an actively studied topic in data mining. A key research issue related to this topic is data segmentation, which influences the overall computational requirements of an algorithm. This paper makes an effort to address this issue in correlated pattern mining. Two novel data segmentation techniques, 'database segmentation' and 'transaction segmentation,' have been introduced to discover the patterns efficiently. The former technique involves segmenting the database into multiple sub-databases such that each sub-database can be mined independently. The latter technique involves segmenting a transaction into multiple sub-transactions such that each sub-transaction can be processed as an individual transaction. The proposed techniques are algorithm independent, and therefore, can be incorporated into any parallel algorithm to find correlated patterns effectively. In this paper, we introduce map-reduce based pattern-growth algorithm by incorporating the above mentioned techniques. Experimental results demonstrate that the proposed algorithm is memory and runtime efficient and highly scalable as well.
Year
DOI
Venue
2018
10.1007/978-3-319-98539-8_27
Lecture Notes in Computer Science
Keywords
Field
DocType
Data mining,Knowledge discovery in databases,Parallel algorithms,Correlated patterns and map-reduce
Data mining,Data segment,Market segmentation,Segmentation,Parallel algorithm,Computer science,Database transaction,Scalability
Conference
Volume
ISSN
Citations 
11031
0302-9743
0
PageRank 
References 
Authors
0.34
13
5
Name
Order
Citations
PageRank
Amulya Kotni100.34
R. Uday Kiran225125.72
Masashi Toyoda338849.87
P. Krishna Reddy410517.26
Masaru Kitsuregawa53188831.46