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Incremental Algorithm for Discovering Frequent Subsequences in Multiple Data Streams

Incremental Algorithm for Discovering Frequent Subsequences in Multiple Data Streams

Reem Al-Mulla, Zaher Al Aghbari
Copyright: © 2011 |Volume: 7 |Issue: 4 |Pages: 20
ISSN: 1548-3924|EISSN: 1548-3932|EISBN13: 9781613506363|DOI: 10.4018/jdwm.2011100101
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MLA

Al-Mulla, Reem, and Zaher Al Aghbari. "Incremental Algorithm for Discovering Frequent Subsequences in Multiple Data Streams." IJDWM vol.7, no.4 2011: pp.1-20. http://doi.org/10.4018/jdwm.2011100101

APA

Al-Mulla, R. & Al Aghbari, Z. (2011). Incremental Algorithm for Discovering Frequent Subsequences in Multiple Data Streams. International Journal of Data Warehousing and Mining (IJDWM), 7(4), 1-20. http://doi.org/10.4018/jdwm.2011100101

Chicago

Al-Mulla, Reem, and Zaher Al Aghbari. "Incremental Algorithm for Discovering Frequent Subsequences in Multiple Data Streams," International Journal of Data Warehousing and Mining (IJDWM) 7, no.4: 1-20. http://doi.org/10.4018/jdwm.2011100101

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Abstract

In recent years, new applications emerged that produce data streams, such as stock data and sensor networks. Therefore, finding frequent subsequences, or clusters of subsequences, in data streams is an essential task in data mining. Data streams are continuous in nature, unbounded in size and have a high arrival rate. Due to these characteristics, traditional clustering algorithms fail to effectively find clusters in data streams. Thus, an efficient incremental algorithm is proposed to find frequent subsequences in multiple data streams. 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.

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