Semi-Supervised Heterogeneous Fusion for Multimedia Data Co-Clustering

Semi-supervised Clustering Using Heterogeneous Dissimilarities

Semi-supervised Clustering on Heterogeneous Information Networks

Semi-supervised clustering on information networks combines both the labeled and unlabeled data sets with an aim to improve the clustering performance. However, the existing semi-supervised clustering methods are all designed for homogeneous networks and do not deal with heterogeneous ones. In this work, we propose a semi-supervised clustering approach to analyze heterogeneous information netwo...

Semi-Supervised Co-Clustering for Query-Oriented Theme-based Summarization

Sentence clustering plays an important role in theme-based summarization which aims to discover the topical themes defined as the clusters of highly related sentences. However, due to the short length of sentences, the word-vector cosine similarity traditionally used for document clustering is no longer suitable. To alleviate this problem, we regard a word as an independent text object rather t...

Fuzzy semi-supervised co-clustering for text documents

In this paper we propose a new heuristic semi-supervised fuzzy co-clustering algorithm (SS-HFCR) for categorization of large web documents. In this approach, the clustering process is carried out by incorporating some prior knowledge in the form of pair-wise constraints provided by users into the fuzzy co-clustering framework. Each constraint specifies whether a pair of documents “must” or “can...

Extracting Prior Knowledge from Data Distribution to Migrate from Blind to Semi-Supervised Clustering

Although many studies have been conducted to improve the clustering efficiency, most of the state-of-art schemes suffer from the lack of robustness and stability. This paper is aimed at proposing an efficient approach to elicit prior knowledge in terms of must-link and cannot-link from the estimated distribution of raw data in order to convert a blind clustering problem into a semi-supervised o...