Clustering is a relevant problem in machine learning where the main goal is to locate meaningful partitions of unlabeled data. In the case of labeled data, a related problem is supervised clustering, where the objective is to locate classuniform clusters. Most current approaches to supervised clustering optimize a score related to cluster purity with respect to class labels. In particular, we p...

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...

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...

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...

This paper presents a latent variable structured prediction model for discriminative supervised clustering of items called the Latent Left-linking Model (LM). We present an online clustering algorithm for LM based on a feature-based item similarity function. We provide a learning framework for estimating the similarity function and present a fast stochastic gradient-based learning technique. In...

This paper is about the evaluation of the results of clustering algorithms, and the comparison of such algorithms. We propose a new method based on the enrichment of a set of independent labeled datasets by the results of clustering, and the use of a supervised method to evaluate the interest of adding such new information to the datasets. We thus adapt the cascade generalization [1] paradigm i...

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 uses the limited background knowledge to aid unsupervised clustering algorithms. Recently, a kernel method for semi-supervised clustering has been introduced, which has been shown to outperform previous semi-supervised clustering approaches. However, the setting of the kernel’s parameter is left to manual tuning, and the chosen value can largely affect the quality of ...