Discovering periodic-frequent patterns in transactional databases

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5476 LNAI, Issue , 2009, Pages 242-253

  Tanbeer, Syed Khairuzzaman ^a   Ahmed, Chowdhury Farhan ^a   Byeong Soo, Jeong ^a   Young Koo, Lee ^a  

^a Kyung Hee University   (South Korea)

Author keywords

Data mining; Frequent pattern; Interesting pattern; Knowledge discovery; Periodic frequent pattern.

Indexed keywords

DATABASE CONTENTS; FREQUENT PATTERN; FREQUENT PATTERN TREE; FREQUENT PATTERNS; INTERESTING PATTERN; INTERESTINGNESS; KNOWLEDGE DISCOVERY; MEMORY EFFICIENT; MINING ALGORITHMS; MINING TECHNIQUES; NOVEL CONCEPT; PATTERN GROWTH; PERFORMANCE STUDY; PERIODIC-FREQUENT PATTERN.; REAL-WORLD SCENARIO; SUPPORT THRESHOLD; TEMPORAL PERIODICITY; TRANSACTIONAL DATABASE; TREE-BASED;

DATA MINING; DATA STRUCTURES; DATABASE SYSTEMS; MINING;

PERIODIC STRUCTURES;

EID: 67650700168     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-01307-2_24     Document Type: Conference Paper

References (10)

1. Agrawal, R., Imielinski, T., Swami, A.N.: Mining Association Rules Between Sets of Items in Large Databases. In: ACM SIGMOD Int. Conf. on Management of Data, pp. 207-216 (1993)
2. Han, J., Pei, J., Yin, Y.: Mining Frequent Patterns without Candidate Generation. In: ACM SIGMOD Int. Conf. on Management of Data, pp. 1-12 (2000)
3. Zaki, M.J., Hsiao, C.-J.: Efficient Algorithms for Mining Closed Itemsets and Their Lattice Structure. IEEE Trans. on Knowl. and Data Eng. 17(4), 462-478 (2005) (Pubitemid 40495590)
4. Zhi-Jun, X., Hong, C., Li, C.: An Efficient Algorithm for Frequent Itemset Mining on Data Streams. In: Int. Conf. on Management of Data, pp. 474-491 (2006) (Pubitemid 44124078)
5. Minh, Q.T., Oyanagi, S., Yamazaki, K.: Mining the K-Most Interesting Frequent Patterns Sequentially. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds.) IDEAL 2006. LNCS, vol.4224, pp. 620-628. Springer, Heidelberg (2006) (Pubitemid 44670422)
6. Tanbeer, S.K., Ahmed, C.F., Jeong, B.-S., Lee, Y.-K.: CP-tree: A Tree Structure for Single-Pass Frequent Pattern Mining. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS(LNAI), vol.5012, pp. 1022-1027. Springer, Heidelberg (2008) (Pubitemid 351776401)
7. Tatavarty, G., Bhatnagar, R., Young, B.Discovery of Temporal Dependencies between Frequent Patterns in Multivariate Time SeriesIn: The 2007 IEEE Symposium on Computational Intelligence and Data Mining, pp. 688-696 (2007) (Pubitemid 47431507)
8. Maqbool, F., Bashir, S., Baig, A.R.: E-MAP: Efficiently Mining Asynchronous Periodic Patterns. Int. J. of Comp. Sc. and Net. Security 6(8A), 174-179 (2006)
9. Hu, T., Sung, S.Y., Xiong, H., Fu, Q.: Discovery of Maximum Length Frequent Itemsets. Information Sciences 178, 69-87 (2008) (Pubitemid 47531788)
10. Wang, H., Perng, C.-S., Ma, S., Yu, P.S.: Demand-driven Frequent Itemset Mining Using Pattern Structures. Knowledge and Information Systems 8, 82-102 (2005) (Pubitemid 40743375)