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Fast Algorithms for Projected Clustering (1999)  (Make Corrections)  (61 citations)
Charu C. Aggarwal, Cecilia Procopiuc, Joel L. Wolf, Philip S. Yu, et al.



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Abstract: The clustering problem is well known in the database literature for its numerous applications in problems such as customer segmentation, classification and trend analysis. Unfortunately, all known algorithms tend to break down in high dimensional spaces because of the inherent sparsity of the points. In such high dimensional spaces not all dimensions may be relevant to a given cluster. One way of handling this is to pick the closely correlated dimensions and find clusters in the corresponding... (Update)

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BibTeX entry:   (Update)

C. C. Aggarwal, C. Procopiuc, J. L. Wolf, P. S. Yu, and J. S. Park. Fast algorithms for projected clustering. In Proceedings of ACM SIGMOD Conference on Management of Data, pages 61--72, 1999. http://citeseer.ist.psu.edu/aggarwal99fast.html   More

@inproceedings{ aggarwal99fast,
    author = "Charu C. Aggarwal and Joel L. Wolf and Philip S. Yu and Cecilia Procopiuc and Jong Soo Park",
    title = "Fast algorithms for projected clustering",
    pages = "61--72",
    year = "1999",
    url = "citeseer.ist.psu.edu/aggarwal99fast.html" }
Citations (may not include all citations):
805   Algorithms for Clustering Data (context) - Jain, Dubes - 1998
475   Automatic Subspace Clustering of High Dimensional Data for D.. - Agrawal, Gehrke et al. - 1998
349   Knowledge Acquisition via Incremental Conceptual Clustering (context) - Fisher - 1987
282   Finding Groups in Data - An Introduction to Cluster Analysis (context) - Kaufman, Rousseeuw - 1990
242   Efficient and Effective Clustering Methods for Spatial Data .. - Ng, Han - 1994
222   BIRCH: An Efficient Data Clustering Method for Very Large Da.. - Zhang, Ramakrishnan et al. - 1996
210   A Density Based Algorithm for Discovering Clusters in Large .. - Ester, Kriegel et al. - 1995
190   word of mouth (context) - Shardanand, Maes et al. - 1995
153   AutoClass: A Bayesian Classification System (context) - Cheeseman, Kelly et al. - 1988
133   CURE: An Efficient Clustering Algorithm for Large Databases - Guha, Rastogi et al. - 1998
121   A cost model for nearest neighbor search in highdimensional .. (context) - Keim, Berchtold et al. - 1997
94   Clustering to minimize the maximum intercluster distance (context) - Gonzalez - 1985
57   Resource Allocation Problems: Algorithmic Approaches (context) - Ibaraki, Katoh - 1988
54   Clustering Categorical Data: An Approach Based on Dynamical .. - Gibson, Kleinberg et al. - 1998
52   Knowledge Discovery in Large Spatial Databases: Focusing Tec.. - Ester, Kriegel et al. - 1995
46   A Database Interface for Clustering in Large Spatial Databas.. (context) - Ester, Kriegel et al. - 1995
36   An Algorithm for Point Clustering and Grid Generation (context) - Berger, Rigoutsos - 1991
33   Clustering Methodologies in Exploratory Data Analysis (context) - Dubes, Jain - 1980
33   Feature Subset Selection Using the Wrapper Method: Overfitti.. - Kohavi, Sommerfield - 1995
25   A Distribution-Based Clustering Algorithm for Mining in Larg.. (context) - Xu, Ester et al. - 1998
24   Optimization and Simplification of Hierarchical Clusters (context) - Fisher - 1995
11   Nearest-Neighbor Graph for Clustering and Outlier Detection (context) - Brito, Chavez et al. - 1997
9   A Comparative Study of Clustering Methods (context) - Zait, Messatfa - 1997
4   Advances in Information Systems Science (context) - Lee, its - 1981
3   A Generalized Histogram Clustering for Multidimensional Imag.. (context) - Wharton - 1983
1   Order Statistics (context) - Hand - 1981



The graph only includes citing articles where the year of publication is known.


Documents on the same site (http://cs.sungshin.ac.kr/~jpark/HOME/refs.html):
Efficient and Effective Clustering Methods for Spatial Data Mining - Ng, Han (1994)   (Correct)
BIRCH: An Efficient Data Clustering Method for Very Large .. - Zhang, Ramakrishnan.. (1996)   (Correct)

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