BibTeX:

@article{IJIRSTV1I7071,
     title={Mining Sequences - Approaches and Analysis},
     author={Manika Verma, Dr.Devarshi Mehta, Dr.Vishal Dahiya and Krupa Mehta},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={1},
     number={7},
     pages={229--233},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV1I7071.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}

Sequential Pattern Mining is to discover sequential patterns, with user-specified minimum support of pattern where support is number of sequences that contains pattern, from a database of sequences. Each sequence of database consists of list of transactions ordered by transaction time and each transaction is a set of items. Closed Sequential Pattern Mining has same capability as Sequential pattern mining, but in Closed Sequential Pattern Mining redundant patterns to be generated and stored are reduced which is much economical. This paper presents approaches and key-feature of algorithms ClaSP, CM-ClaSP, CloSpan, BIDE which are used for mining closed sequential patterns as well as approaches and key features of algorithms GSP, SPADE, PrefixSpan, SPAM, LAPIN which are used for mining sequential pattern. It shows that number of sequences generated in Closed Sequential Pattern Mining is much less than those generated by Sequential Pattern Mining which makes Closed Sequential Pattern Mining Economical. The algorithms are compared by attributes total time required to find frequent sequences, number of frequent sequences generated and maximum memory required.

Sequential Pattern Mining, Closed Sequential Pattern Mining.