AGRID: An efficient algorithm for clustering large high-dimensional datasets

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 2637, Issue , 2003, Pages 271-282

  Zhao, Yanchang ^a   Song, Junde ^a  

^a BEIJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

Author keywords

Clustering; Data mining; Dimensionality; Grid; Iso density line

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DATA MINING; KNOWLEDGE ENGINEERING;

CLUSTERING ALGORITHM; HIGH-DIMENSIONAL DATASETS; NON-PARAMETRIC ALGORITHM; SCALABILITY;

DATA REDUCTION; CLUSTERING ALGORITHMS;

CLUSTERING; DENSITY-BASED CLUSTERING; DIMENSIONALITY; GRID; HIGH DIMENSIONAL DATASETS; HIGH SCALABILITIES; ISO-DENSITY LINE; NON-PARAMETRIC ALGORITHM;

EID: 7444237650     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. M. Ankerst, M. Breunig, et al, "OPTICS: Ordering points to identify the clustering structure", In Proc. 1999 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD'99), pp. 49-60, Philadelphia, PA, June 1999.
2. R. Agrawal, J. Gehrke, et al, "Automatic subspace clustering of high dimensional data for data mining aplications", In Proc. 1998 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD'98), pp. 94-105, Seattle, WA, June 1998.
3. K. Alsabti, S. Ranka, V. Singh, "An Efficient K-Means Clustering Algorithm," Proc. the First Workshop on High Performance Data Mining, Orlando, Florida, 1998.
4. P.S. Bradley, O.L. Mangasarian, "K-Plane Clustering," Journal of Global Optimization 16, Number 1, 2000, pp. 23-32.
5. M. Ester, H.-P. Kriegel et al, "A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise," in Proc.1996 Int. Conf. On Knowledge Discovery and Data Mining (KDD'96), pp. 226-231, Portland, Oregon, Aug. 1996.
6. S. Guha, R. Rastogi, and K. Shim, "Cure: An efficient clustering algorithm for large databases", In Proc. 1998 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD'98), pp. 73-84, Seattle, WA, June 1998.
7. S. Guha, R. Rastogi, and K. Shim, "Rock: A robust clustering algorithm for categorical attributes", In Proc. 1999 Int. Conf. Data Engineering (ICDE'99), pp. 512-521, Sydney, Australia, Mar. 1999.
8. A. Hinneburg and D.A. Keim, "An efficient approach to clustering in large multimedia databases with noise", In Proc. 1998 Int. Conf. Knowledge Discovery and Data Mining (KDD'98), pp. 58-65, New York, Aug. 1998.
9. Jiawei Han, Micheline Kamber, "Data Mining: Concepts and Techniques," Higher Education Press, Morgan Kaufmann Publishers, 2001.
10. Z. Huang, "Extensions to the k-means algorithm for clustering large data sets with categorical values", Data Mining and Knowledge Discovery, 2: 283-304, 1998.
11. Zhexue Huang, "A Fast Clustering Algorithm to Cluster Very Large Categorical Data Sets in Data Mining," in SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery (SIGMOD-DMKD'97), Tucson, Arizona, May 1997.
12. G. Karypis, E.-H. Han, and V. Kumar, "CHAMELEON: A hierarchical clustering algorithm using dynamic modeling", IEEE Computer, Special Issue on Data Analysis and Mining, Vol. 32, No. 8, August 1999, pp. 68-75.
13. R. Ng and J. Han, "Efficient and effective clustering method for spatial data mining", In Proc. 1994 Int. Conf. Very Large Data Bases (VLDB'94), pp. 144-155, Santiago, Chile, Sept. 1994.
14. G. Sheikholeslami, S. Chatterjee, and A. Zhang, "WaveCluster: A multi-resolution clustering approach for very large spatial databases", In Proc. 1998 Int. Conf. Very Large Data Bases (VLDB'98), pp. 428-429, New York, Aug. 1998.
15. Jörg Sander, Martin Ester, Hans-Peter Kriegel, Xiaowei Xu: "Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications", Data Mining and Knowledge Discovery, Vol. 2, No 2, June 1998.
16. W. Wang, J. Yang, and R. Muntz, "STING: A statistical information grid approach to spatial data mining", In Proc. 1997 Int. Conf. Very Large Data Bases (VLDB'97), pp. 186-195, Athens, Greece, Aug. 1997.
17. T. Zhang, R. Ramakrishnan, and M. Livny, "BIRCH: An efficient data clustering method for very large databases", In Proc. 1996 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD'96), pp. 103-114, Montreal, Canada, June 1996.
18. Zhao Yanchang, Song Junde, "GDILC: A Grid-based Density Iso-line Clustering Algorithm," Proc. Int. Conf. Info-tech and Info-net (ICII 2001), Beijing, China, Oct. 2001.