Hybrid Clustering Algorithm Based on Mahalanobis Distance and MST

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Provided by: International Journal of Applied Information Systems (IJAIS)
Topic: Data Management
Format: PDF
Most of the clustering algorithms are based on euclidean distance as measure of similarity between data objects. Theses algorithms also require initial setting of parameters as a prior, for example the number of clusters. The euclidean distance is very sensitive to scales of variables involved and independent of correlated variables. To conquer these drawbacks a hybrid clustering algorithm based on mahalanobis distance is proposed in this paper. The reason for the hybridization is to relieve the user from setting the parameters in advance.
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