Accelerating k-medoid-based algorithms through metric access methods

Authors:
Maria Camila N. Barioni;Humberto L. Razente;Agma J. M. Traina;Caetano Traina, Jr.
Affiliations:
Computer Sciences Department, ICMC/USP, Caixa Postal 668, 13560-970 São Carlos, SP, Brazil;Computer Sciences Department, ICMC/USP, Caixa Postal 668, 13560-970 São Carlos, SP, Brazil;Computer Sciences Department, ICMC/USP, Caixa Postal 668, 13560-970 São Carlos, SP, Brazil;Computer Sciences Department, ICMC/USP, Caixa Postal 668, 13560-970 São Carlos, SP, Brazil
Venue:
Journal of Systems and Software
Year:
2008

Citing 13
Cited 2

The R*-tree: an efficient and robust access method for points and rectangles

SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
BIRCH: an efficient data clustering method for very large databases

SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases

SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Data clustering: a review

ACM Computing Surveys (CSUR)
Fast Indexing and Visualization of Metric Data Sets using Slim-Trees

IEEE Transactions on Knowledge and Data Engineering
CLARANS: A Method for Clustering Objects for Spatial Data Mining

IEEE Transactions on Knowledge and Data Engineering
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces

VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Efficient and Effective Clustering Methods for Spatial Data Mining

VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An Efficient K -Medoids-Based Algorithm Using Previous Medoid Index, Triangular Inequality Elimination Criteria, and Partial Distance Search

DaWaK 2000 Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery
Knowledge Discovery in Large Spatial Databases: Focusing Techniques for Efficient Class Identification

SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
The Metric Histogram: A New and Efficient Approach for Content-based Image Retrieval

Proceedings of the IFIP TC2/WG2.6 Sixth Working Conference on Visual Database Systems: Visual and Multimedia Information Management
A top-down approach for density-based clustering using multidimensional indexes

Journal of Systems and Software - Special issue: Performance modeling and analysis of computer systems and networks
A new and efficient k-medoid algorithm for spatial clustering

ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part III

K-Medoids-Based Random Biometric Pattern for Cryptographic Key Generation

CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Effective retrieval and new indexing method for case based reasoning: Application in chemical process design

Engineering Applications of Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

Scalable data mining algorithms have become crucial to efficiently support KDD processes on large databases. In this paper, we address the task of scaling up k-medoid-based algorithms through the utilization of metric access methods, allowing clustering algorithms to be executed by database management systems in a fraction of the time usually required by the traditional approaches. We also present an optimization strategy that can be applied as an additional step of the proposed algorithm in order to achieve better clustering solutions. Experimental results based on several datasets, including synthetic and real ones, show that the proposed algorithm can reduce the number of distance calculations by a factor of more than three thousand times when compared to existing algorithms, while producing clusters of equivalent quality.