Semi-supervised clustering employs a small amount of labeled data to aid unsupervised learning. Previous work in the area has employed one of two approaches: 1) Searchbased methods that utilize supervised data to guide the search for the best clustering, and 2) Similarity-based methods that use supervised data to adapt the underlying similarity metric used by the clustering algorithm. This pape...

MOTIVATION Patients with identical cancer diagnoses often progress differently. The disparity we see in disease progression and treatment response can be attributed to the idea that two histologically similar cancers may be completely different diseases on the molecular level. Methods for identifying cancer subtypes associated with patient survival have the capacity to be powerful instruments f...

In this paper, we define a new notion for a clustering to be useful, called actionable clustering. This notion is motivated by applications across various domains such as in business, education, public policy and healthcare. We formalize this notion by adding a novel constraint to traditional unsupervised clustering. We argue that this notion is different from semi-supervised clustering, superv...

In this paper, we propose to adapt the batch version of selforganizing map (SOM) to background information in clustering task. It deals with constrained clustering with SOM in a deterministic paradigm. In this context we adapt the appropriate topological clustering to pairwise instance level constraints with the study of their informativeness and coherence properties for measuring their utility...

Many applications are facing the problem of learning from an objective dataset, whereas information from other auxiliary sources may be beneficial but cannot be integrated into the objective dataset for learning. In this paper, we propose an omni-view learning approach to enable learning from multiple data collections. The theme is to organize heterogeneous data sources into a unified table wit...

This paper presents a semi-supervised kernel-based fuzzy c-means algorithm called S2KFCM by introducing semi-supervised learning technique and the kernel method simultaneously into conventional fuzzy clustering algorithm. Through using labeled and unlabeled data together, S2KFCM can be applied to both clustering and classification tasks. However, only the latter is concerned in this paper. Expe...

This paper presents an approach for image annotation with relevance feedback that interactively employs a semi-supervised learning to build hierarchical classifiers associated with annotation labels. We construct individual hierarchical classifiers each corresponding to one semantic label that is used for describing the semantic contents of the images. We adopt hierarchical approach for classif...

We consider the task of semi-supervised classification: extending category labels from a small dataset of labeled examples to a much larger set. We show that, at least on our case study task, unsupervised fuzzy clustering of the unlabeled examples helps in obtaining the hard clusters. Namely, we used the membership values obtained with fuzzy clustering as additional features for hard clustering...

A popular approach to semi-supervised learning proceeds by endowing the inputdata with a graph structure in order to extract geometric information and incorporate it intoa Bayesian framework. We introduce new theory that gives appropriate scalings of graphparameters that provably lead to a well-defined limiting posterior as the size of the unlabeleddata set grows. Furthermore, w...

We address an issue of semi-supervised learning on multiple graphs, over which informative subgraphs are distributed. One application under this setting can be found in molecular biology, where different types of gene networks are generated depending upon experiments. Here an important problem is to annotate unknown genes by using functionally known genes, which connect to unknown genes in gene...