Abstract
Sequential Pattern Mining is an efficient technique for discovering recurring structures or patterns from very large dataset. Many algorithms are proposed for mining. Broadly data mining algorithms are classified into two categories as Pattern-Growth approach or candidate generation and Apriori –Based. By introducing constraints such as user defined threshold, user specified data, minimum gap or time, algorithms outperforms better. In this paper we have used dataset of protein sequences and comparison in between PrefixSpan from pattern growth approach and SPAM from Apriori-Based algorithm. This comparative study is carried out with respect to space and time consumption of an algorithm. The study shows that SPAM with constraints outperforms better than PrefixSpan for very large dataset but for smaller data PrefixSpan works better than SPAM.
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Mane, R.V. (2013). A Comparative Study of Spam and PrefixSpan Sequential Pattern Mining Algorithm for Protein Sequences. In: Unnikrishnan, S., Surve, S., Bhoir, D. (eds) Advances in Computing, Communication, and Control. ICAC3 2013. Communications in Computer and Information Science, vol 361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36321-4_13
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DOI: https://doi.org/10.1007/978-3-642-36321-4_13
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