Abstract
One of the main challenges in the design of modern clustering algorithms is that, in many applications, new data sets are continuously added into an already huge database. As a result, it is impractical to carry out data clustering from scratch whenever there are new data instances added into the database. One way to tackle this challenge is to incorporate a clustering algorithm that operates incrementally. Another desirable feature of clustering algorithms is that a clustering dendrogram is generated. This feature is crucial for many applications in biological, social, and behavior studies, due to the need to construct taxonomies. This paper presents the GRIN algorithm, an incremental hierarchical clustering algorithm for numerical data sets based on gravity theory in physics. The GRIN algorithm delivers favorite clustering quality and generally features O(n) time complexity. One main factor that makes the GRIN algorithm be able to deliver favorite clustering quality is that the optimal parameters settings in the GRIN algorithm are not sensitive to the distribution of the data set. On the other hand, many modern clustering algorithms suffer unreliable or poor clustering quality when the data set contains highly skewed local distributions so that no optimal values can be found for some global parameters. This paper also reports the experiments conducted to study the characteristics of the GRIN algorithm.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
M. Charikar, C. Chekuri, T. Feder and R. Motwani: Incremental Clustering and Dynamic Information Retrieval. In Proceedings of the 29th Annual ACM Symposium on Theory of Computing (STOC-97), 1997, pp. 626–634.
M. Ester, H.-P. Kriegel, J. Sander, M. Wimmer, and X. Xu. Incremental clustering for mining in a data warehousing environment. In Proceedings of 24th International Conference on Very Large Data Bases (VLDB-98), 1998, pp. 323–333.
B. Everitt, Cluster analysis, New York: Halsted Press, 1980.
D. Fisher, Improving inference through conceptual clustering, In Proceedings of 6th National Conference on Artificial Intelligence (AAAI-87), 1987, pp. 461–465.
J. Gennari, P. Langley, and D. Fisher, Models of incremental concept formation, Artificial Intelligence, vol. 40, pp. 11–61, 1989.
J. Han, M. Kamber, Data Mining: Concepts and Techniques, San Francisco: Morgan Kaufmann Publishers, 2000.
R. V. Hogg and E. A. Tanis, Probability and statistical inference, New Jersey: Prentice Hall, 2001.
A.K. Jain, R.C. Dubes, Algorithms for clustering data, Englewood Cliffs, N.J.: Prentice Hall, 1988.
A.K. Jain, M.N. Murty, P.J. Flynn, Data Clustering: A Review, ACM Computing Surveys, vol. 31, no. 3, pp. 264–323, 1999.
Yen-Jen Oyang, Chien-Yu Chen, and Tsui-Wei Yang, A Study on the Hierarchical Data Clustering Algorithm Based on Gravity Theory, In Proceedings of 5th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD-01), 2001, pp. 350–361.
Yen-Jen Oyang, Chien-Yu Chen, Shien-Ching Hwang, and Cheng-Fang Lin, Characteristics of a Hierarchical Data Clustering Algorithm Based on Gravity Theory, Technical Report of NTUCSIE 02-01. (Available at http://mars.csie.ntu.edu.tw/~cychen/publications_on_dm.htm )
A. Ribert, A. Ennaji, and Y. Lecourtier, An incremental Hierarchical Clustering, In Proceedings of 1999 Vision Interface Conference, 1999, pp. 586–591.
M. Stonebraker, J. Frew, K. Gardels and J. Meredith, The Sequoia 2000 Storage Benchmark, In Proceedings of 1993 ACM-SIGMOD International Conference on Management of Data (SIGMOD-93), 1993, pp. 2–11.
I. H. Witten, Data mining: practical machine learning tools and techniques with Java implementations, San Francisco, Califonia: Morgan Kaufmann, 2000.
T. Zhang, R. Ramakrishnan, M. Livny, BIRCH: An Efficient Data Clustering Method for Very Large Databases, In Proceedings of the 1996 ACM-SIGMOD International Conference on Management of Data (SOGMOD-96), Jun. 1996, pp. 103–114.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, CY., Hwang, SC., Oyang, YJ. (2002). An Incremental Hierarchical Data Clustering Algorithm Based on Gravity Theory. In: Chen, MS., Yu, P.S., Liu, B. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2002. Lecture Notes in Computer Science(), vol 2336. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-47887-6_23
Download citation
DOI: https://doi.org/10.1007/3-540-47887-6_23
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-43704-8
Online ISBN: 978-3-540-47887-4
eBook Packages: Springer Book Archive