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Sequence Pattern Mining in Data Streams

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dc.contributor.advisor Saheb, Mahmoud
dc.contributor.author Hijawi, Hamza
dc.contributor.author Saheb, Mahmoud
dc.date.accessioned 2017-08-10T10:29:25Z
dc.date.available 2017-08-10T10:29:25Z
dc.date.issued 2015-08-01
dc.identifier.citation H. M. Hijawi, M. H. Saheb, Vol 8, No 3, August 2015, (2015),Sequence Pattern Mining in Data Streams, Computer and Information Science ,ISSN 1913-8989 (Print) ISSN 1913-8997 (Online), DOI: 10.5539/cis.v8n3p64, http://ccsenet.org/journal/index.php/cis/article/view/48654 en_US
dc.identifier.issn 913-8989
dc.identifier.issn 1913-8997
dc.identifier.uri http://ccsenet.org/journal/index.php/cis/article/view/48654
dc.identifier.uri http://scholar.ppu.edu/handle/123456789/325
dc.description.abstract Sequential pattern mining in data streams environment is an interesting data mining problem. The problem of finding sequential patterns in static databases had been studied extensively in the past years, however mining sequential patterns in the data streams still an active field for researches. In this research a new greedy sequence pattern mining algorithm for the data streams is introduced, it will be used to find the strongly supported sequences. The proposed algorithm is built based on the sequence tree which is used to find the sequential patterns in static databases. The proposed algorithm divides the streams into patches or windows and each patch will update the sequence tree which built from the previous windows. An example is introduced to explain how this algorithm works. We also show the efficiency and the effectiveness of the proposed algorithm on a synthetic dataset and prove how it is suited for data streams environment. We showed experimentally that the proposed algorithm is more efficient than the PrefixSpan algorithm for patterns with any support less than 30% for CPU time and with any support less than 60% for memory usage en_US
dc.language.iso en en_US
dc.publisher Computer and Information Science en_US
dc.relation.ispartofseries cis.v8n3;64
dc.subject sequential patterns mining en_US
dc.subject data streams en_US
dc.subject sequence mining en_US
dc.subject sequence tree en_US
dc.title Sequence Pattern Mining in Data Streams en_US
dc.type Article en_US


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