Abstract:

This paper presents an MSTbased semisupervised clustering method of adaptively optimizing dissimilarity, when clustering an unlabeled data set which has the same or a similar distribution with a labeled sample in one hybrid attributes space. First, we can obtain “regular cluster regions” by using a decisiontree method, and then adaptively optimize the dissimilarity of the hybrid attributes space based on the principia, “〖WTBX〗data points in the same clusters should have more similarity than those in other clusters〖WTBZ〗”. Finally, the optimized dissimilarity is applied to an MSTbased clustering method. From some simulated experiments of several UCI data sets, we know that this kind of semisupervised clustering method can often get better clustering quality. In the end, it gives a research expectation to disinter and popularize this method.

Key words: dissimilarity;semisupervised clustering;hybrid attributes