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Pushing Constraints to Generate Top-K Closed Sequential Graph Patterns

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
K. Vijay Bhaskar, K. Thammi Reddy, S. Sumalatha
10.5120/ijca2016908818

Vijay K Bhaskar, Thammi K Reddy and S Sumalatha. Article: Pushing Constraints to Generate Top-K Closed Sequential Graph Patterns. International Journal of Computer Applications 137(7):34-42, March 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {K. Vijay Bhaskar and K. Thammi Reddy and S. Sumalatha},
	title = {Article: Pushing Constraints to Generate Top-K Closed Sequential Graph Patterns},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {137},
	number = {7},
	pages = {34-42},
	month = {March},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

In this paper, the problem of finding sequential patterns from graph databases is investigated. Two serious issues dealt in this paper are efficiency and effectiveness of mining algorithm. A huge volume of sequential patterns has been generated out of which most of them are uninteresting. The users have to go through a large number of patterns to find interesting results. In order to improve the efficiency and effectiveness of the mining process, constraints are more essential. Constraint-based mining is used in many fields of data mining such as frequent pattern mining, sequential pattern mining, and subgraph mining. A novel algorithm called CSGP (Constraint-based Sequential Graph Pattern mining) is proposed for mining interesting sequential patterns from graph databases. CSGP algorithm is revised to mine top-k closed patterns and named as TCSGP (Top-k Closed constraint-based Sequential Graph Pattern mining).

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Keywords

Sequential patterns, Closed patterns, Constraints.