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Survey on Enhanced Sequential Pattern Mining
Mehta Devshri,  Madhuri Vaghasia
Data mining is the method or the activity of analyzing data from different perspectives and summarizing it into useful information. Among different tasks in data mining, sequential pattern mining is one of the most important tasks. Sequential pattern mining involves the mining of the subsequences that appear frequently in a set of sequences. Sequential pattern mining algorithms using a vertical representation are the most efficient for mining sequential patterns in long sequences, and have excellent overall performance. The vertical representation allows generating patterns and calculating their supports without performing costly database scans. i.e. only one database scan is used. However, a crucial performance bottleneck of vertical algorithms is that they use a generate candidate and test approach that can generate a large amount of infrequent candidates. To address this issue, we use pruning candidates based on the study of item co-occurrences. There is a new structure named CMAP (Co-occurrence MAP) for storing co-occurrence information.
Keywords- Sequential Pattern, Minimum support, vertical database format, SPAM, Co-occurrence information
Unique Identification Number - IJEDR1702149Page Number(s) - 890-895Pubished in - Volume 5 | Issue 2 | May 2017DOI (Digital Object Identifier) -    Publisher - IJEDR (ISSN - 2321-9939)
Cite this Article
Mehta Devshri,  Madhuri Vaghasia,   "Survey on Enhanced Sequential Pattern Mining"
, International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.5, Issue 2, pp.890-895, May 2017, Available at :http://www.ijedr.org/papers/IJEDR1702149.pdf