PrefixSpan: Mining Sequential Patterns by Prefix-Projected Pattern
Poonam Sharma, Gudla. Balakrishna · International Journal of Computer Science & Engineering Survey · 2011
Sequential pattern mining discovers frequent subsequences as patterns in a sequence database.Most of the previously developed sequential pattern mining methods, such as GSP, explore a candidate generation-and-test approach [1] to reduce the number of candidates to be examined.However, this approach may not be efficient in mining large sequence databases having numerous patterns and/or long patterns.In this paper, we propose a projection-based, sequential pattern-growth approach for efficient mining of sequential patterns.In this approach, a sequence database is recursively projected into a set of smaller projected databases, and sequential patterns are grown in each projected database by exploring only locally frequent fragments.Based on an initial study of the pattern growth-based sequential pattern mining, FreeSpan, we propose a more efficient method, called PSP, which offers ordered growth and reduced projected databases technique is developed in PrefixSpan.