메뉴 건너뛰기




Volumn 17, Issue 5, 2008, Pages 1321-1344

Mining top-k frequent patterns in the presence of the memory constraint

Author keywords

[No Author keywords available]

Indexed keywords


EID: 46749099518     PISSN: 10668888     EISSN: 0949877X     Source Type: Journal    
DOI: 10.1007/s00778-007-0078-6     Document Type: Article
Times cited : (56)

References (27)
  • 2
  • 4
    • 0031162961 scopus 로고    scopus 로고
    • Dynamic itemset counting and implication rules for market basket data
    • Brin, S., Motwani, R., Ullman, J., Tsur, S.: Dynamic itemset counting and implication rules for market basket data. In: Proceedings of SIGMOD (1997)
    • (1997) Proceedings of SIGMOD
    • Brin, S.1    Motwani, R.2    Ullman, J.3    Tsur, S.4
  • 6
    • 33745610726 scopus 로고    scopus 로고
    • Mining association rules without support threshold: With and without item constraints
    • Cheung, Y.L., Fu, A.W.: Mining association rules without support threshold: with and without item constraints. In: TKDE (2004)
    • (2004) TKDE
    • Cheung, Y.L.1    Fu, A.W.2
  • 7
    • 19544377965 scopus 로고    scopus 로고
    • Moment: Maintaining closed frequent itemsets over a stream sliding window
    • Chi, Y., Wang, H., Yu, P.S., Muntz, R.R.: Moment: maintaining closed frequent itemsets over a stream sliding window. In: Proceedings of ICDM (2004)
    • (2004) Proceedings of ICDM
    • Chi, Y.1    Wang, H.2    Yu, P.S.3    Muntz, R.R.4
  • 8
    • 23944472645 scopus 로고    scopus 로고
    • Tight upper bounds on the number of candidate patterns
    • 2
    • Geerts, F., Goethals, B., Bussche, J.V.D. (2005) Tight upper bounds on the number of candidate patterns. ACM Trans. Database Syst. 30(2): 333-363
    • (2005) ACM Trans. Database Syst , vol.30 , pp. 333-363
    • Geerts, F.1    Goethals, B.2    Bussche, J.V.D.3
  • 10
    • 2442606606 scopus 로고    scopus 로고
    • Memory issues in frequent itemset mining
    • Goethals, B.: Memory issues in frequent itemset mining. In: Proceedings of SAC (2004)
    • (2004) Proceedings of SAC
    • Goethals, B.1
  • 12
    • 0039253846 scopus 로고    scopus 로고
    • Mining frequent patterns without candidate generation
    • Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proceedings of ACM SIGMOD (2000)
    • (2000) Proceedings of ACM SIGMOD
    • Han, J.1    Pei, J.2    Yin, Y.3
  • 13
    • 2442449952 scopus 로고    scopus 로고
    • Mining frequent patterns without candidate generation: A frequent-pattern tree approach
    • Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: a frequent-pattern tree approach. In: DMKD (2004)
    • (2004) DMKD
    • Han, J.1    Pei, J.2    Yin, Y.3    Mao, R.4
  • 14
    • 0002784345 scopus 로고    scopus 로고
    • Algorithms for association rule mining-a general survey and comparison
    • Hipp, J., Guntzer, U., Nakhaeizadeh, G.: Algorithms for association rule mining-a general survey and comparison. In: SIGKDD Explorations (2000)
    • (2000) SIGKDD Explorations
    • Hipp, J.1    Guntzer, U.2    Nakhaeizadeh, G.3
  • 15
    • 0346354497 scopus 로고    scopus 로고
    • Approximate frequency counts over streaming data
    • Manku, G.S., Motwani, R.: Approximate frequency counts over streaming data. In: Proceedings of VLDB (2002)
    • (2002) Proceedings of VLDB
    • Manku, G.S.1    Motwani, R.2
  • 17
    • 46749112226 scopus 로고    scopus 로고
    • KDCI: A multi-strategy algorithm for mining frequent sets
    • Orlando, S., Lucchese, C., Palmerini, P., Perego, R., Silvestri, F.: kDCI: a multi-strategy algorithm for mining frequent sets. In: Proceedings of Workshop on Frequent Itemset Mining Implementations (2004)
    • (2004) Proceedings of Workshop on Frequent Itemset Mining Implementations
  • 19
  • 20
    • 0031220368 scopus 로고    scopus 로고
    • Using a hash-based method with transaction trimming for mining association rules
    • Park, J.S., Chen, M.-S., Yu, P.S.: Using a hash-based method with transaction trimming for mining association rules. IEEE Trans. Knowl. Data Eng. 9(5), (1997)
    • (1997) IEEE Trans. Knowl. Data Eng. , vol.9 , Issue.5
    • Park, J.S.1    Chen, M.-S.2    Yu, P.S.3
  • 22
    • 19944376126 scopus 로고    scopus 로고
    • TFP: An efficient algorithm for mining top-k frequent closed itemsets
    • Wang, J., Han, J., Lu, Y., Tzvetkov, P.: TFP: an efficient algorithm for mining top-k frequent closed itemsets. In: TKDE (2005)
    • (2005) TKDE
    • Wang, J.1    Han, J.2    Lu, Y.3    Tzvetkov, P.4
  • 23
    • 84880100631 scopus 로고    scopus 로고
    • Mining top-k itemsets over a sliding window based on zipfian distribution
    • Wong, R.C.-W., Fu, A.W.: Mining top-k itemsets over a sliding window based on zipfian distribution. In: Proceedings of SIAM SDM (2005)
    • (2005) Proceedings of SIAM SDM
    • Wong, R.C.-W.1    Fu, A.W.2
  • 24
    • 79953873919 scopus 로고    scopus 로고
    • Considering main memory in mining association rules
    • Xiao, Y., Dunham, M.H.: Considering main memory in mining association rules. In: Proceedings of DAWAK (1999)
    • (1999) Proceedings of DAWAK
    • Xiao, Y.1    Dunham, M.H.2
  • 25
    • 79953236929 scopus 로고    scopus 로고
    • False positive or false negative: Mining frequent itemsets from high speed transactional data streams
    • Yu, J.X., Chong, Z., Lu, H., Zhou, A.: False positive or false negative: mining frequent itemsets from high speed transactional data streams. In: Proceedings of VLDB (2004)
    • (2004) Proceedings of VLDB
    • Yu, J.X.1    Chong, Z.2    Lu, H.3    Zhou, A.4
  • 26
    • 0347709684 scopus 로고    scopus 로고
    • Charm: An efficient algorithm for closed itemset mining
    • Zaki, M.J., Hsiao, C.-J.: Charm: an efficient algorithm for closed itemset mining. In: Proceedings of SIAM SDM (2002)
    • (2002) Proceedings of SIAM SDM
    • Zaki, M.J.1    Hsiao, C.-J.2
  • 27
    • 0035788918 scopus 로고    scopus 로고
    • Real world performance of association rule algorithms
    • Zheng, Z., Kohavi, R., Mason, L.: Real world performance of association rule algorithms. In: Proceedings of SIGKDD (2001)
    • (2001) Proceedings of SIGKDD


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.