Supervised and unsupervised learning have been the focus of critical research in the areas
of machine learning and artificial intelligence. In the literature, these two streams flow …

This paper addresses the problem of learning to classify texts by exploiting information
derived from clustering both training and testing sets. The incorporation of knowledge …

This paper addresses the problem of learning to classify textsby exploiting information
derived from clustering both training and testing sets. The incorporation of knowledge …

Automatic organization of documents has become an important research issue since the
explosion of digital and online text information. There are mainly two machine learning …

Text clustering is a useful and inexpensive way to organize vast text repositories into
meaningful topics categories. However, there is little consensus on which clustering …

This paper presents a novel approach for adapting the complexity of a text categorization
system to the difficulty of the task. In this study, we adapt a simple text classifier (Rocchio) …

In this informative age, we find many documents are available in digital forms which need
classification of the text. For solving this major problem present researchers focused on …

This paper proposes an approach called TESC (TExt classification using Semi-supervised
Clustering) to improve text classification. The basic idea is to regard one category of texts …

Cluster analysis refers to a family of procedures which are fundamentally concerned with
automatically arranging data into meaningful groups. These procedures are increasingly …

The automated categorization (or classification) of texts into predefined categories has
witnessed a booming interest in the last 10 years, due to the increased availability of …