K-means is a widely used partitional clustering method. While there are considerable
research efforts to characterize the key features of K-means clustering, further investigation …

The phrase “data mining” was termed in the late eighties of the last century, which describes
the activity that attempts to extract interesting patterns from data. Since then, data mining and …

Document clustering can be applied in document organisation and browsing, document
summarisation and classification. The identification of an appropriate representation for …

Johnson--Lindenstrauss Transforms are powerful tools for reducing the dimensionality of
data while preserving key characteristics of that data, and they have found use in many …

A very important category of clustering methods is hierarchical clustering. There are
considerable research efforts which have been focused on algorithm-level improvements of …

Document clustering techniques have been applied in several areas, with the web as one of
the most recent and influential. Both general-purpose and text-oriented techniques exist and …

The state-of-the-art text clustering methods suffer from the huge size of documents with high-
dimensional features. In this paper, we studied fast SOM clustering technology for Text …

A key challenge for document clustering consists in finding a proper similarity measure for
text documents that enables the generation of cohesive groups. Measures based on the …

Ideally, document clustering methods should produce clusters that are semantically relevant
and readily understandable as collections of documents belonging to particular contexts or …

Partitioning vast amounts of text documents is a challenging problem due to a high
dimensional representation of the documents. In this study, we investigate the quality of text …