Two notable applications of text classification are sentiment analysis and spam filtering.
Traditional machine learning approaches to text classification are often complex, non …

Clustering aided classification methods are based on the assumption that the learned
clusters under the guidance of initial training data can somewhat characterize the underlying …

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 …

In this paper, a new Cluster based Classification Model (CCM) for suspicious email
detection and other text classification tasks, is presented. Comparative experiments of the …

This article presents silhouette–attraction (Sil–Att), a simple and effective method for text
clustering, which is based on two main concepts: the silhouette coefficient and the idea of …

Clustering and Classification are significant and widely used task in data mining. Their
incorporation together is rare. When we integrate them together they can give more …

In the paper we present a cluster based approach for terrorist network evolution. We have
applied hierarchical agglomerative clustering approach to 9/11 case study. We show that …

Web search results are far from perfect due to the polysemous and synonymous
characteristics of nature languages, information overload as the results of information …

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 …

A machine learning based model aimed at automation of the indexing and routing of the
incoming documents of the enterprise is proposed in this article. An overall automation …