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Volumn , Issue , 2007, Pages 350-359

Finding low-entropy sets and trees from binary data

Author keywords

Local models; Pattern discovery

Indexed keywords

DEPENDENCY STRUCTURE; LOCAL MODELS; PATTERN DISCOVERY;

EID: 36849033944     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1281192.1281232     Document Type: Conference Paper
Times cited : (33)

References (24)
  • 1
    • 0032090765 scopus 로고    scopus 로고
    • Automatic subspace clustering of high dimensional data for data mining applications
    • AGRAWAL, R., GEHRKE, J., GUNOPULOS, D., AND RAGHAVAN, P. Automatic subspace clustering of high dimensional data for data mining applications. In SIGMOD Conference (1998), pp. 94-105.
    • (1998) SIGMOD Conference , pp. 94-105
    • AGRAWAL, R.1    GEHRKE, J.2    GUNOPULOS, D.3    RAGHAVAN, P.4
  • 2
    • 0027621699 scopus 로고
    • Mining association rules between sets of items in large databases
    • AGRAWAL, R., IMIELINSKI, T., AND SWAMI, A. N. Mining association rules between sets of items in large databases. In SIGMOD Conference (1993), pp. 207-216.
    • (1993) SIGMOD Conference , pp. 207-216
    • AGRAWAL, R.1    IMIELINSKI, T.2    SWAMI, A.N.3
  • 6
    • 84933530882 scopus 로고
    • Approximating discrete probability distributions with dependence trees
    • CHOW, C. K., AND LIU, C. N. Approximating discrete probability distributions with dependence trees. IEEE Trans. Info. Theory 14, 3 (1968), 462-467.
    • (1968) IEEE Trans. Info. Theory , vol.14 , Issue.3 , pp. 462-467
    • CHOW, C.K.1    LIU, C.N.2
  • 7
    • 34249832377 scopus 로고
    • Bayesian method for the induction of probabilistic networks from data
    • COOPER, G. F., AND HERSKOVITS, E. A Bayesian method for the induction of probabilistic networks from data. Machine Learning 9 (1992), 309-347.
    • (1992) Machine Learning , vol.9 , pp. 309-347
    • COOPER, G.F.1    HERSKOVITS, E.A.2
  • 9
    • 0016102310 scopus 로고
    • A projection pursuit algorithm for exploratory data analysis
    • FRIEDMAN, J. H., AND TUKEY, J. W. A projection pursuit algorithm for exploratory data analysis. IEEE Trans. Comput. 23 (1974), 881-890.
    • (1974) IEEE Trans. Comput , vol.23 , pp. 881-890
    • FRIEDMAN, J.H.1    TUKEY, J.W.2
  • 10
    • 33745037411 scopus 로고    scopus 로고
    • Fragments of order
    • GIONIS, A., KUJALA, T., AND MANNILA, H. Fragments of order. In KDD (2003), pp. 129-136.
    • (2003) KDD , pp. 129-136
    • GIONIS, A.1    KUJALA, T.2    MANNILA, H.3
  • 11
    • 33749583834 scopus 로고    scopus 로고
    • Assessing data mining results via swap randomization
    • GIONIS, A., MANNILA, H., MIELIKÄINEN, T., AND TSAPARAS, P. Assessing data mining results via swap randomization. In KDD (2006), pp. 167-176.
    • (2006) KDD , pp. 167-176
    • GIONIS, A.1    MANNILA, H.2    MIELIKÄINEN, T.3    TSAPARAS, P.4
  • 13
    • 34249761849 scopus 로고
    • Learning Bayesian networks: The combination of knowledge and statistical data
    • HECKERMAN, D., GEIGER, D., AND CHICKERING, D. M. Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning 20, 3 (1995), 197-243.
    • (1995) Machine Learning , vol.20 , Issue.3 , pp. 197-243
    • HECKERMAN, D.1    GEIGER, D.2    CHICKERING, D.M.3
  • 14
    • 33750377696 scopus 로고    scopus 로고
    • Finding trees from unordered 0-1 data
    • HEIKINHEIMO, H., MANNILA, H., AND SEPPÄNEN, J. K. Finding trees from unordered 0-1 data. In PKDD (2006), pp. 175-186.
    • (2006) PKDD , pp. 175-186
    • HEIKINHEIMO, H.1    MANNILA, H.2    SEPPÄNEN, J.K.3
  • 15
    • 0000263797 scopus 로고
    • Projection pursuit
    • June
    • HUBER, P. J. Projection pursuit. The Annals of Statistics 13, 2 (June 1985), 435-475.
    • (1985) The Annals of Statistics , vol.13 , Issue.2 , pp. 435-475
    • HUBER, P.J.1
  • 16
    • 33749555022 scopus 로고    scopus 로고
    • Maximally informative k-itemsets and their efficient discovery
    • KNOBBE, A. J., AND HO, E. K. Y. Maximally informative k-itemsets and their efficient discovery. In KDD (2006), pp. 237-244.
    • (2006) KDD , pp. 237-244
    • KNOBBE, A.J.1    HO, E.K.Y.2
  • 17
    • 69249216907 scopus 로고    scopus 로고
    • Advances in exact Bayesian structure discovery in Bayesian networks
    • KOIVISTO, M. Advances in exact Bayesian structure discovery in Bayesian networks. In UAI (2006), pp. 241-248.
    • (2006) UAI , pp. 241-248
    • KOIVISTO, M.1
  • 19
    • 0033687894 scopus 로고    scopus 로고
    • Traversing itemset lattice with statistical metric pruning
    • ACM, pp
    • MORISHITA, S., AND SESE, J. Traversing itemset lattice with statistical metric pruning. In PODS (2000), ACM, pp. 226-236.
    • (2000) PODS , pp. 226-236
    • MORISHITA, S.1    SESE, J.2
  • 20
    • 17044376078 scopus 로고    scopus 로고
    • Subspace clustering for high dimensional data: A review
    • PARSONS, L., HAQUE, E., AND LIU, H. Subspace clustering for high dimensional data: a review. SIGKDD Explor. Newsl. 6, 1 (2004), 90-105.
    • (2004) SIGKDD Explor. Newsl , vol.6 , Issue.1 , pp. 90-105
    • PARSONS, L.1    HAQUE, E.2    LIU, H.3
  • 22
    • 36849037021 scopus 로고    scopus 로고
    • SIEBES, A., VREEKEN, J., AND VAN LEEUWEN, M. Item sets that compress. In SDM (2006), J. Ghosh, D. Lambert, D. B. Skillicorn, and J. Srivastava, Eds., SIAM.
    • SIEBES, A., VREEKEN, J., AND VAN LEEUWEN, M. Item sets that compress. In SDM (2006), J. Ghosh, D. Lambert, D. B. Skillicorn, and J. Srivastava, Eds., SIAM.
  • 23
    • 33750304892 scopus 로고    scopus 로고
    • Compression picks item sets that matter
    • VAN LEEUWEN, M., VREEKEN, J., AND SIEBES, A. Compression picks item sets that matter. In PKDD (2006), pp. 585-592.
    • (2006) PKDD , pp. 585-592
    • VAN LEEUWEN, M.1    VREEKEN, J.2    SIEBES, A.3
  • 24
    • 35048886120 scopus 로고    scopus 로고
    • ZIMMERMANN, A., AND DE RAEDT, L. CorClass: Correlated association rule mining for classification. In Discovery Science (2004), E. Suzuki and S. Arikawa, Eds., 3245 of Lecture Notes in Computer Science, Springer, pp. 60-72.
    • ZIMMERMANN, A., AND DE RAEDT, L. CorClass: Correlated association rule mining for classification. In Discovery Science (2004), E. Suzuki and S. Arikawa, Eds., vol. 3245 of Lecture Notes in Computer Science, Springer, pp. 60-72.


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.