Information Retrieval
On Clustering Validation Techniques
Journal of Intelligent Information Systems
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
MUC4 '92 Proceedings of the 4th conference on Message understanding
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Comparing clusterings: an axiomatic view
ICML '05 Proceedings of the 22nd international conference on Machine learning
A comparison of extrinsic clustering evaluation metrics based on formal constraints
Information Retrieval
A novel measure for validating clustering results applied to road traffic
Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data
A novel measure for validating clustering results applied to road traffic
Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Clustering validity measures aim to evaluate the goodness of clustering results in order to find the best partition. Results are obtained by varying the input parameters values. However, sometimes, the values generated by these measures are very close and the choice of the optimal value associated to the best partition may be meaningless. In this paper, we propose a new concept called hybrid strategy to resolve this problem. This concept is based on the use of two measures. The first measure aims to analyse the goodness of each partition obtained with different values of input parameters. The use of the second measure permits to select the best partition between those having good but very close values of the first measure. To illustrate this strategy, we propose a new hybrid measure ---called "HS-measure"--- based on Homogeneity degree and Silhouette coefficient. The performance of our measure is then tested on road traffic data set.