Abstract In data clustering, many approaches have been proposed. For example, K-means method and hierarchical method. A problem is in effect by initial value and criterion to comb...
Spectral data often have a large number of highly-correlated features, making feature selection both necessary and uneasy. A methodology combining hierarchical constrained clusteri...
EM algorithm is an important unsupervised clustering algorithm, but the algorithm has several limitations. In this paper, we propose a fast EM algorithm (FEMA) to address the limi...
We present a novel algorithm for agglomerative hierarchical clustering based on evaluating marginal likelihoods of a probabilistic model. This algorithm has several advantages ove...
Organizing Web search results into a hierarchy of topics and subtopics facilitates browsing the collection and locating results of interest. In this paper, we propose a new hierar...