On Knowledge-Enhanced Document Clustering

In text analytics (Srivastava & Sahami 2009), document clustering refers to the problem of automatically grouping documents into different groups (known as clusters), such that documents in one cluster are similar to each other while being dissimilar from the ones in a different cluster. Typically, the dataset is represented using the vector model in which a set of m documents with n unique words form an m x n document-word matrix (Baeza-Yates & Ribeiro-Neto, 2011). An entry ij in the matrix denotes the frequency of the word j in document i. Document clustering methods can be categorized into two approaches, viz., partitional and hierarchical clustering. Partitional or flat clustering directly divides the set of documents into different clusters. Hierarchical clustering on the other hand creates a tree-like structure ABSTRACT