CSBIterKmeans: A New Clustering Algorithm Based on Quantitative Assessment of the Clustering Quality

Authors:
Tarek Smaoui;Sascha Müller;Christian Müller-Schloer
Affiliations:
Leibniz Universität Hannover - Institute of Systems Engineering, Hannover, Germany 30167;Leibniz Universität Hannover - Institute of Systems Engineering, Hannover, Germany 30167;Leibniz Universität Hannover - Institute of Systems Engineering, Hannover, Germany 30167
Venue:
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Year:
2009

Citing 8
Cited 0

Self-organization and associative memory: 3rd edition

Self-organization and associative memory: 3rd edition
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 mining: concepts and techniques

Data mining: concepts and techniques
Knowledge Acquisition Via Incremental Conceptual Clustering

Machine Learning
WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases

VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
STING: A Statistical Information Grid Approach to Spatial Data Mining

VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Data Clustering: Theory, Algorithms, and Applications (ASA-SIAM Series on Statistics and Applied Probability)

Data Clustering: Theory, Algorithms, and Applications (ASA-SIAM Series on Statistics and Applied Probability)

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we introduce a clustering algorithm CSBIterKmeans based on the well-known k -means algorithm. Our approach is based on the validation of the clustering result by combining two "antipodal" validation metrics, cluster separation and cluster compactness, to determine autonomously the "best" number of clusters and hence dispense with the number of clusters as input parameter. We report about our first results with a collection of audio features extracted from songs and discuss the performance of the algorithm with different numbers of features and objects.