메뉴 건너뛰기




Volumn 4093 LNAI, Issue , 2006, Pages 436-447

ExMiner: An efficient algorithm for mining Top-K frequent patterns

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATA REDUCTION; DATA STRUCTURES; INFORMATION ANALYSIS; PROBLEM SOLVING; DATA MINING;

EID: 33749401329     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11811305_48     Document Type: Conference Paper
Times cited : (25)

References (12)
  • 1
    • 0001882616 scopus 로고
    • Fast algorithm for mining association rules
    • Santiago, Chille
    • Agrawal, R, and Srikant, R.: Fast algorithm for mining association rules. In proc. of VLDB '94, Santiago, Chille (1994) 487-499
    • (1994) Proc. of VLDB '94 , pp. 487-499
    • Agrawal, R.1    Srikant, R.2
  • 6
    • 0002625450 scopus 로고    scopus 로고
    • CLOSET: An efficient algorithm from mining frequent closed itemsets
    • Pei, J., Han, J., and Mao, R.: CLOSET: An efficient algorithm from mining frequent closed itemsets. In proc. of DMKD'00 (2000)
    • (2000) Proc. of DMKD'00
    • Pei, J.1    Han, J.2    Mao, R.3
  • 10
    • 19944376126 scopus 로고    scopus 로고
    • TFP: An efficient algorithm for mining top-k frequent closed itemsets
    • Wang, J., Han, J., Lu, Y. and Tzvetkov, P.: TFP: An efficient algorithm for mining top-k frequent closed itemsets. In proc. of IEEE Knowledge and Data Engineering, vol 17, no.5 (2005) 652-663
    • (2005) Proc. of IEEE Knowledge and Data Engineering , vol.17 , Issue.5 , pp. 652-663
    • Wang, J.1    Han, J.2    Lu, Y.3    Tzvetkov, P.4
  • 11
    • 33749403369 scopus 로고    scopus 로고
    • TF2P-growth: An efficient algorithm for mining frequent patterns without any thresholds
    • Hirate, Y., Iwahashi, E., and Yamana, H.: TF2P-growth: An efficient algorithm for mining frequent patterns without any thresholds. In proc. of ICDM (2004)
    • (2004) Proc. of ICDM
    • Hirate, Y.1    Iwahashi, E.2    Yamana, H.3
  • 12
    • 0346299592 scopus 로고    scopus 로고
    • IBM Quest Data Mining Project. Quest synthetic data generation http://almaden.ibm.com/software/quest/Resources/index.shtnu
    • Quest Synthetic Data Generation


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.