Effective periodic pattern mining in time series databases

Nishi, Manziba Akanda; Ahmed, Chowdhury Farhan; Samiullah, M.; Jeong, Byeongmoon

doi:10.1016/j.eswa.2012.12.017

Cited by 49 publications

(43 citation statements)

References 14 publications

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“…One of the existing approaches, proposed in Nishi et al (2013), can fairly generate patterns like {a ðnÞ θ c}. However, the approach is not efficient enough, as it uses apriori based sequential mining approach to produce periodic patterns.…”

Section: Introductionmentioning

confidence: 99%

“…The method has some limitations similar to apriori based sequential mining algorithms like complexities in mining long sequences, such as, for P length pattern mining, at worst case, 2 P number of patterns need to be handled and at least P number of times the database needs to be scanned. Moreover, generation and testing of false patterns and such patterns are quite large in number, as well as huge running time and computational memory usage also hinder the success of flexible periodic pattern mining using the existing algorithm (Nishi et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

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An efficient approach to mine flexible periodic patterns in time series databases

Chanda

Saha

Nishi

et al. 2015

Engineering Applications of Artificial Intelligence

Self Cite

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An efficient approach to mine flexible periodic patterns in time series databases

Chanda

Saha

Nishi

et al. 2015

Engineering Applications of Artificial Intelligence

Self Cite

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“…For instance, suffix tree based algorithm [9], that is, if we use the suffix tree to generate patterns and detect periodicity, we will fail to generate some flexible and interesting patterns. Then, Manziba et al [10] proposed new algorithm has overcome this limitation. Recently, there are many existing algorithms about periodic patterns mining.…”

Section: Periodic Patterns Miningmentioning

confidence: 99%

Research on Patterns Mining Method for Moving Objects

Chen¹,

Liu²,

Chen³

et al. 2016

Advanced Science and Technology Letters

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Abstract. Advanced technology in GPS and sensors enables us to track moving objects, such as human beings, animals, vehicles. These mobility data as historical activity data of moving objects, in some degree can reflect some internal and external features of moving objects, how to use the massive high-precision mobile data identify potential and meaningful pattern is the current hot spots and is also a serious problem. Patterns mining also have numerous applications in human mobility understanding, urban planning and ecological studies and a wide variety of other fields. In this paper, we provide a general perspective for studies on the issues of patterns mining by reviewing the methods and algorithms in detail and comparing the existing results on the same issues, providing a quick understanding of research to the worker.

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“…For instance, suffix tree based algorithm [37], that is, if we use the suffix tree to generate patterns and detect periodicity, we will fail to generate some flexible and interesting patterns. Then, Manziba et al [38] proposed new algorithm has overcome this limitation. Recently, there are many existing algorithms about periodic patterns mining.…”

Section: Periodic Patternsmentioning

confidence: 99%