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Mining Recurring Patterns in Time Series

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dc.contributor.author Bhalodiya, Dharmesh
dc.contributor.author Tadhani, Jaydeep
dc.contributor.author Davda, Rajesh
dc.date.accessioned 2023-05-16T05:22:11Z
dc.date.available 2023-05-16T05:22:11Z
dc.date.issued 2019-05
dc.identifier.citation Bhalodiya, 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.issn 0975 - 8887
dc.identifier.uri http://10.9.150.37:8080/dspace//handle/atmiyauni/962
dc.description.abstract Periodic 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 consumptions en_US
dc.language.iso en en_US
dc.publisher International Journal of Computer Applications en_US
dc.subject Recurring Patterns en_US
dc.subject RP-tree en_US
dc.subject Time Series en_US
dc.title Mining Recurring Patterns in Time Series en_US
dc.type Article en_US


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