TY - GEN
T1 - Extensions for continuous pattern mining
AU - Gorawski, Marcin
AU - Jureczek, Pawel
PY - 2011
Y1 - 2011
N2 - In this paper we present extensions for continuous pattern mining. Our previous continuous pattern mining algorithm mines the set of all frequent sequences satisfying the minSup condition. However, those sequences contain an explosive number of frequent subsequences, which makes the analysis and understanding of patterns very difficult. In order to overcome these difficulties, we propose four new algorithms for mining maximal and closed continuous patterns. These algorithms return a superset of the result patterns and then a post-pruning algorithm is performed to eliminate redundant sequences. For each type of patterns (maximal or closed) two algorithms are presented (with and without some improvements). The key idea is to omit as many redundant sequences as possible during the exploration. The proposed algorithms allow one to reduce the size of the result set when input sequences have low uniqueness.
AB - In this paper we present extensions for continuous pattern mining. Our previous continuous pattern mining algorithm mines the set of all frequent sequences satisfying the minSup condition. However, those sequences contain an explosive number of frequent subsequences, which makes the analysis and understanding of patterns very difficult. In order to overcome these difficulties, we propose four new algorithms for mining maximal and closed continuous patterns. These algorithms return a superset of the result patterns and then a post-pruning algorithm is performed to eliminate redundant sequences. For each type of patterns (maximal or closed) two algorithms are presented (with and without some improvements). The key idea is to omit as many redundant sequences as possible during the exploration. The proposed algorithms allow one to reduce the size of the result set when input sequences have low uniqueness.
UR - https://www.scopus.com/pages/publications/80053008004
U2 - 10.1007/978-3-642-23878-9_24
DO - 10.1007/978-3-642-23878-9_24
M3 - Conference contribution
AN - SCOPUS:80053008004
SN - 9783642238772
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 194
EP - 203
BT - Intelligent Data Engineering and Automated Learning, IDEAL 2011 - 12th International Conference, Proceedings
T2 - 12th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2011
Y2 - 7 September 2011 through 9 September 2011
ER -