A Semi-supervised approach to Document Clustering with Sequence Constraints

Document clustering is usually performed as an unsupervised task. It attempts to separate different groups of documents (clusters) from a document collection based on implicitly identifying the common patterns present in these documents. A semi-supervised approach to this problem recently reported promising results. In semi-supervised approach, an explicit background knowledge (for example: Must-link or Cannot-link information for a pair of documents) is used in the form of constraints to drive the clustering process in the right direction. In this paper, a semi-supervised approach to document clustering is proposed. There are three main contributions through this paper (i) a document is transformed primarily into a graph representation based on Graph-of-Word approach. From this graph, a word sequences of size=3 is extracted. This sequence is used as a feature for the semi-supervised clustering. (ii) A similarity function based on commonword sequences is proposed, and (iii) the constrained based algorithm is designed to perform the actual cluster process through active learning. The proposed algorithm is implemented and extensively tested on three standard text mining datasets. The method clearly outperforms the recently proposed algorithms for document clustering in term of standard evaluation measures for document clustering task.