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2012•International journal of innovative computing, information & controlRequires access

A SINGLE-SCAN ALGORITHM FOR MINING SEQUENTIAL PATTERNS FROM DATA STREAMS

Hua-Fu Li, Chin-Chuan Ho, Hsuan-Sheng Chen, Suh-Yin Lee

Open publisher page 5 citations

Abstract

Sequential pattern mining (SPAM) is one of the most interesting research issues of data mining. In this paper, a new research problem of mining data streams for sequential patterns is dened. A data stream is an unbound sequence of data ele- ments arriving at a rapid rate. Based on the characteristics of data streams, the problem complexity of mining data streams for sequential patterns is more difficult than that of mining sequential patterns from large static databases. Therefore, mining sequential patterns from data streams is a challenging research issue of data mining and knowl- edge discovery. Hence, an efficient single-pass algorithm, called

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What this paper is about

Sequential pattern mining (SPAM) is one of the most interesting research issues of data mining. In this paper, a new research problem of mining data streams for sequential patterns is dened. A data stream is an unbound sequence of data ele- ments arriving at a rapid rate. Based on the characteristics of data streams, the problem complexity of mining data streams for sequential patterns is more difficult than that of mining sequential patterns from large static databases. Therefore, mining sequential patterns from data streams is a challenging research issue of data mining and knowl- edge discovery. Hence, an efficient single-pass algorithm, called

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OpenAlex reports 5 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Sequential pattern mining (SPAM) is one of the most interesting research issues of data mining. In this paper, a new research problem of mining data streams for sequential patterns is dened. A data stream is an unbound sequence of data ele- ments arriving at a rapid rate. Based on the characteristics of data streams, the problem complexity of mining data streams for sequential patterns is more difficult than that of mining sequential patterns from large static databases. Therefore, mining sequential patterns from data streams is a challenging research issue of data mining and knowl- edge discovery. Hence, an efficient single-pass algorithm, called

Key concepts: Data stream mining, Computer science, Data mining, Data stream, Sequential Pattern Mining, STREAMS, Sequence (biology), Algorithm

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