Periodic Pattern Mining a Algorithms and Applications

Periodic Pattern Mining a Algorithms and Applications

G.N.V.G. Sirisha

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M. Shashi

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G.V. Padma Raju

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1 S.R.K.R. Engineering College

GJCST Volume 13 Issue C13

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The objective of our study was to evaluate, in a population of Togolese People Living With HIV(PLWHIV), the agreement between three scores derived from the general population namely the Framingham score, the Systematic Coronary Risk Evaluation (SCORE), the evaluation of the cardiovascular risk (CVR) according to the World Health Organization.

Owing to a large number of applications periodic pattern mining has been extensively studied for over a decade. Periodic pattern is a pattern that repeats itself with a specific period in a give sequence. Periodic patterns can be mined from datasets like biological sequences, continuous and discrete time series data, spatiotemporal data and social networks. Periodic patterns are classified based on different criteria. Periodic patterns are categorized as frequent periodic patterns and statistically significant patterns based on the frequency of occurrence. Frequent periodic patterns are in turn classified as perfect and imperfect periodic patterns, full and partial periodic patterns, synchronous and asynchronous periodic patterns, dense periodic patterns, approximate periodic patterns. This paper presents a survey of the state of art research on periodic pattern mining algorithms and their application areas. A discussion of merits and demerits of these algorithms was given. The paper also presents a brief overview of algorithms that can be applied for specific types of datasets like spatiotemporal data and social networks.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

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Not applicable for this article.

G.N.V.G. Sirisha. 2014. \u201cPeriodic Pattern Mining a Algorithms and Applications\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 13 (GJCST Volume 13 Issue C13): .

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GJCST Volume 13 Issue C13
Pg. 19- 28

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

approximate periodic patterns DNA sequences partial periodic patterns periodic pattern mining S sequence mining spatiotemporal data time series

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Submission ReceivedDecember 13, 2012
RevisedDecember 13, 2012
Peer Review Double Blind
Handling Editor
Accepted January 5, 2013
Published January 15, 2013

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Issue date

January 26, 2014

Language

English

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Total Score: 103

Country: India

Subject: Global Journal of Computer Science and Technology - C: Software & Data Engineering

Authors: G.N.V.G. Sirisha, M. Shashi, G.V. Padma Raju (PhD/Dr. count: 0)

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