A semi-supervised clustering algorithm that integrates heterogeneous dissimilarities and data sources

摘要

Clustering algorithms depend strongly on the dissimilarity considered to evaluate the sample proximities. In real applications, several dissimilarities are available that may come from different object representations or data sources. Each dissimilarity provides usually complementary information about the problem. Therefore, they should be integrated in order to reflect accurately the object proximities.