Please use this identifier to cite or link to this item: http://10.9.150.37:8080/dspace//handle/atmiyauni/962
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dc.contributor.authorBhalodiya, Dharmesh-
dc.contributor.authorTadhani, Jaydeep-
dc.contributor.authorDavda, Rajesh-
dc.date.accessioned2023-05-16T05:22:11Z-
dc.date.available2023-05-16T05:22:11Z-
dc.date.issued2019-05-
dc.identifier.citationBhalodiya, D. ,Tadhani, J. ,and Davda, R.(2019).Mining Recurring Patterns in Time Series. International Journal of Computer Applications, 198(11), 1-4, ISSN 0975 - 8887.en_US
dc.identifier.issn0975 - 8887-
dc.identifier.urihttp://10.9.150.37:8080/dspace//handle/atmiyauni/962-
dc.description.abstractPeriodic pattern mining consists of finding patterns that exhibit either complete or partial cyclic repetitions in a time series. Past studies on partial periodic search focused on finding regular patterns, i.e., patterns exhibiting either complete or partial cyclic repetitions throughout a series. An example regular pattern of Bat, Ball stats that customers have been purchasing items Bat and Ball alost ev ery day throughout the year. The type of partial periodic pattern is recurring patens, i.e., patterns exhibiting cyclic repetitions only for particular time intervals within a series. Its a very difficult task to identify those periodic frequent patterns within given threshold in time. To overcome these problem, we introduced modification in traditional PR-tree structure. And this structure improves overall efficiency by running time, Periodic Frequent Pattern generation and Memory consumptionsen_US
dc.language.isoenen_US
dc.publisherInternational Journal of Computer Applicationsen_US
dc.subjectRecurring Patternsen_US
dc.subjectRP-treeen_US
dc.subjectTime Seriesen_US
dc.titleMining Recurring Patterns in Time Seriesen_US
dc.typeArticleen_US
Appears in Collections:01. Journal Articles

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