A Survey on Sequential Data Mining: Exploring the Redundant Patterns from Sequences to Minimize the Overall Patterns.

Especially in genomics and proteomics discovering patterns from sequence data has plays an most important role in many aspects of science and society. Multiple strings as input sequence data and substrings as patterns. In the real world, their will be a large set of patterns would discovered many of them are repeated, thus degrading the output quality. For sequential data mining different method and techniques are implemented. These papers make study on different types of sequential data mining techniques (CISP mining technique). Sequence synthesis and recognition of patterns for multiple sequences was proposed by various algorithm such as A-close algorithm, Associaation Rules, MineTCFI, CFI2TCFI Algorithms, Biomolucer sequence i.e DNA and RNA sequence, GSP and Apriori All algorithm, Prefix span algorithm. To efficiently discover these patterns in very large sequence data, two efficient algorithms have been developed through innovative use of suffix tree. Discovery of delta closed patterns (DDCP) and Discovery of non-induced patterns (DNIP) are better as compare to existing algorithm and methods for sequential data mining of data patterns.