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The described approach for finding frequent subsequences is by clustering subsequences of a data stream. The proposed algorithm uses a window model to buffer the continuous data streams. Further, it does not recompute the clustering results for the whole data stream at every window, but rather it builds on clustering results of previous windows. The proposed approach also employs a decay value for each discovered cluster to determine when to remove old clusters and retain recent ones. In addition, the proposed algorithm is efficient as it scans the data streams once and it is considered an Any-time algorithm since the frequent subsequences are ready at the end of every window.<\/p>","DOI":"10.4018\/jdwm.2011100101","type":"journal-article","created":{"date-parts":[[2011,10,19]],"date-time":"2011-10-19T16:11:46Z","timestamp":1319040706000},"page":"1-20","source":"Crossref","is-referenced-by-count":2,"title":["Incremental Algorithm for Discovering Frequent Subsequences in Multiple Data Streams"],"prefix":"10.4018","volume":"7","author":[{"given":"Reem","family":"Al-Mulla","sequence":"first","affiliation":[{"name":"University of Sharjah, UAE"}]},{"given":"Zaher","family":"Al Aghbari","sequence":"additional","affiliation":[{"name":"University of Sharjah, UAE"}]}],"member":"2432","reference":[{"key":"jdwm.2011100101-0","doi-asserted-by":"publisher","DOI":"10.1504\/IJBIDM.2007.012945"},{"key":"jdwm.2011100101-1","doi-asserted-by":"crossref","unstructured":"Barouni-Ebrahimi, M., & Ghorbani, A. 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