Open AccessAn Efficient Closed Maximal Pattern Sequences Mining on High Dimensional Datasets
Author(s) -
Jayesh Krishna,
M Haritha
Publication year - 2019
Publication title -
asian journal of computer science and technology
Language(s) - English
Resource type - Journals
eISSN - 2583-7907
pISSN - 2249-0701
DOI - 10.51983/ajcst-2019.8.s3.2088
Subject(s) - set (abstract data type) , prefix , data mining , sequence (biology) , computer science , span (engineering) , algorithm , pattern recognition (psychology) , artificial intelligence , engineering , biology , genetics , philosophy , programming language , linguistics , civil engineering
Previous methods have presented convincing arguments that mining complete set of patterns is huge for effective usage. A compact but high quality set of patterns, such as closed patterns and maximal patterns is needed. Most of the previously maximal pattern sequences mining algorithms on high dimensional sequence, such as biological data set, work under the same support. In this paper, an efficient algorithm Closed Maximal Pattern Sequences (CMPS-Mine) for mining closed maximal patterns based on multi-support is suggested. Careful exhibitions once Beta-globin gene sequences have exhibited that CMPS-Mine expends less memory utilization and run time over Prefix Span. It generates compacted outcomes and two kinds of interesting patterns.