Pairwise relational information is a useful way of providing partial supervision in domains where class labels are difficult to acquire. This work presents clustering model that incorporates pairwise annotations the form must-link and cannot-link relations considers possible annotation inaccuracies (i.e., common setting when experts provide supervision). We propose generative assumes Gaussian-d...

One of the intuitions underlying many graph-based methods for clustering and semi-supervised learning, is that class or cluster boundaries pass through areas of low probability density. In this paper we provide some formal analysis of that notion for a probability distribution. We introduce a notion of weighted boundary volume, which measures the length of the class/cluster boundary weighted by...

In order to solve the difficult questions such as in the presence of the cluster deviation and high dimensional data processing in traditional semi-supervised clustering algorithm, a semi-supervised clustering algorithm based on active learning was proposed, this algorithm can effectively solve the above two problems. Using active learning strategies in algorithm can obtain a large amount of in...

Many studies in data mining have proposed a new learning called semi-Supervised. Such type of learning combines unlabeled and labeled data which are hard to obtain. However, in unsupervised methods, the only unlabeled data are used. The problem of significance and the effectiveness of semi-supervised clustering results is becoming of main importance. This paper pursues the thesis that muchgreat...

Semi-supervised clustering aims to improve clustering performance by considering user-provided side information in the form of pairwise constraints. We study the active learning problem of selecting must-link and cannot-link pairwise constraints for semi-supervised clustering. We consider active learning in an iterative framework; each iteration queries are selected based on the current cluster...

Key factors like similarity, proximity, and good Many researchers have mentioned the significance of perceptual grouping and organization in vision and listed various continuation that guide to visual grouping of image. However, even to the present situation, many of the computational factors of perceptual grouping have remained unanswered. As there are several probable partitions of the domain...

We propose a supervised word sense disambiguation (WSD) system that uses features obtained from clustering results of word instances. Our approach is novel in that we employ semi-supervised clustering that controls the fluctuation of the centroid of a cluster, and we select seed instances by considering the frequency distribution of word senses and exclude outliers when we introduce “must-link”...

Most of the previous works that disambiguate personal names in Web search results often employ agglomerative clustering approaches. In contrast, we have adopted a semi-supervised clustering approach in order to guide the clustering more appropriately. Our proposed semi-supervised clustering approach is novel in that it controls the fluctuation of the centroid of a cluster, and achieved a purity...

Semi-supervised classification has become an active topic recently and a number of algorithms, such as Self-training, have been proposed to improve the performance of supervised classification using unlabeled data. In this paper, we propose a semi-supervised learning framework which combines clustering and classification. Our motivation is that clustering analysis is a powerful knowledge-discov...

Semi-supervised clustering approaches have emerged as an option for enhancing clustering results. These algorithms use external information to guide the clustering process. In particular, semi-supervised hierarchical clustering approaches have been explored in many fields in the last years. These algorithms provide efficient and personalized hierarchical overviews of datasets. To the best of th...