DensEst: Density estimation for data mining in high dimensional spaces

Society for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics

Volumn 1, Issue , 2009, Pages 172-183

  Müller, Emmanuel ^a,b   Assent, Ira ^a,b   Krieger, Ralph ^a,b   Günnemann, Stephan ^a,b   Seidl, Thomas ^a,b  

^a RWTH AACHEN UNIVERSITY   (Germany)

^b AALBORG UNIVERSITY   (Denmark)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY IMPROVEMENT; ACCURATE ESTIMATION; BOTTOM-UP FASHION; DATA BASE; DENSITY ESTIMATION; DENSITY ESTIMATOR; ESTIMATION QUALITY; FREQUENT ITEMSET MINING; HIGH DIMENSIONAL SPACES; LARGE HIGH-DIMENSIONAL DATA; STEP-BY-STEP; SUB-SPACES; SUBSPACE CLUSTERING; TOPDOWN;

CLUSTERING ALGORITHMS; DATA MINING;

ESTIMATION;

EID: 72849151744     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (23)

1. R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan. Automatic subspace clustering of high dimensional data for data mining applications. In SIGMOD, pages 94-105, 1998.
2. R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In VLDB, pages 487-499, 1994.
3. I. Assent, R. Krieger, E. Müller, and T. Seidl. DUSC: Dimensionality unbiased subspace clustering. In ICDM, pages 409-414, 2007.
4. I. Assent, R. Krieger, E. Müller, and T. Seidl. EDSC: Efficient density-based subspace clustering. In ACM CIKM, 2008.
5. I. Assent, R. Krieger, E. Müller, and T. Seidl. INSCY: indexing subspace clusters with in-process-removal of redundancy. In ICDM, 2008.
6. K. Beyer, J. Goldstein, R. Ramakrishnan, and U. Shaft. When is nearest neighbors meaningful. In ICDT, pages 217-235, 1999.
7. Y. M. Bishop, S. E. Fienberg, and P. W. Holland. Discrete multivariate analysis: Theory and Practice. MIT Press, Cambridge, 1995.
8. B. Bringmann and A. Zimmermann. The chosen few: On identifying valuable patterns. In ICDM, pages 63-72, 2007.
9. Frequent Itemset Mining Dataset Repository. http://fimi.cs.helsinki.fi/, 2008.
10. J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, 2001.
11. J. Han, J. Pei, and Y. Yin. Mining frequent patterns without candidate generation. In SIGMOD, pages 1-12, 2000.
12. K. Kailing, H.-P. Kriegel, and P. Kroger. Density-connected subspace clustering for high-dimensional data. In SDM, pages 246-257, 2004.
13. E. Keogh, X. Xi, L. Wei, and C. A. Ratanamahatana. The ucr time series classification/clustering homepage, 2006.
14. H.-P. Kriegel, P. Kröger, M. Renz, and S. Wurst. A generic framework for efficient subspace clustering of high-dimensional data. In ICDM, pages 250-257, 2005.
15. V. Markl, N. Megiddo, M. Kutsch, T. M. Tran, P. J. Haas, and U. Srivastava. Consistently estimating the selectivity of conjuncts of predicates. In VLDB, pages 373-384, 2005.
16. G. Moise, J. Sander, and M. Ester. P3C: A robust projected clustering algorithm. In ICDM, pages 414-425, 2006.
17. D. Newman, S. Hettich, C. Blake, and C. Merz. UCI repository of MLDBs, 1998.
18. T. Palpanas and N. Koudas. Entropy based approximate querying and exploration of datacubes. In SSDBM, pages 81-90, 2001.
19. J. Pearl. Causality: Models, Reasoning, and Inference. Cambridge University Press, 2000.
20. V. Poosala, P. J. Haas, Y. E. Ioannidis, and E. J. Shekita. Improved histograms for selectivity estimation of range predicates. SIGMOD Rec., 25(2):294-305, 1996.
21. J. Whittaker. Graphical Models in Applied Multivariate Statistics. John Wiley & Sons, 1990.
22. I. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, USA, 2005.
23. F. Zhu, X. Yan, J. Han, P. Yu, and H. Cheng. Mining Colossal Frequent Patterns by Core Pattern Fusion. In ICDE, pages 706-715, 2007.