Clustering algorithms are increasingly employed for the categorization of image databases, in order to provide users with database overviews and make their access more effective. By including information provided by the user, the categorization process can produce results that come closer to user’s expectations. To make such a semi-supervised categorization approach acceptable for the user, thi...

Semi-supervised clustering algorithms are increasingly employed for discovering hidden structure in data with partially labelled patterns. In order to make the clustering approach useful and acceptable to users, the information provided must be simple, natural and limited in number. To improve recognition capability, we apply an effective feature enhancement procedure to the entire data-set to ...

While classification rules are essential in supervised classification methods, they are not noticed well in methods of clustering. Nevertheless, some clustering techniques have clear rules of classification, while they are not obvious in other methods. This paper discusses classification rules or classification functions in the former class including K-means, fuzzy c-means, and the mixture of d...

The exploration of domain knowledge to improve the mining process begins to give its first results. For example, the use of domaindriven constraints allows the focusing of the discovery process on more useful patterns, from the user’s point of view. Semi-supervised clustering is a technique that partitions unlabeled data by making use of domain knowledge, usually expressed as pairwise constrain...

Clustering is the task of grouping a set of examples so that similar examples are grouped into the same cluster while dissimilar examples are in different clusters. The quality of a clustering depends on two problem-dependent factors which are i) the chosen similarity metric and ii) the data representation. Supervised clustering approaches, which exploit labeled partitioned datasets have thus b...

Clustering is the task of grouping a set of examples so that similar examples are grouped into the same cluster while dissimilar examples are in different clusters. The quality of a clustering depends on two problem-dependent factors which are i) the chosen similarity metric and ii) the data representation. Supervised clustering approaches, which exploit labeled partitioned datasets have thus b...

We consider the problem of clustering partially labeled data from a minimal number of randomly chosen pairwise comparisons between the items. We introduce an efficient local algorithm based on a power iteration of the non-backtracking operator and study its performance on a simple model. For the case of two clusters, we give bounds on the classification error and show that a small error can be ...

Feature selection has been extensively used in supervised learning, such as text classification. It (Devaney and Ram 1997) minimizes the high dimensionality of the feature space and also offers improved data understanding which enhances the clustering result. The chosen feature set should consist of adequate data about the original data set. It is believed that feature selection can enhance the...

Semi-supervised is the machine learning field. In the previous work, selection of pairwise constraints for semi-supervised clustering is resolved using active learning method in an iterative manner. Semi-supervised clustering derived from the pairwise constraints. The pairwise constraint depends on the two kinds of constraints such as must-link and cannot-link.In this system, enhanced iterative...