Title: Tree-based text stream clustering with application to spam mail classification

Authors: Phimphaka Taninpong; Sudsanguan Ngamsuriyaroj

Addresses: Faculty of Information and Communication Technology, Mahidol University, Salaya, Nakorn Pathom 73170, Thailand ' Faculty of Information and Communication Technology, Mahidol University, Salaya, Nakorn Pathom 73170, Thailand

Abstract: This paper proposes a new text clustering algorithm based on a tree structure. The main idea of the clustering algorithm is a sub-tree at a specific node represents a document cluster. Our clustering algorithm is a single pass scanning algorithm which traverses down the tree to search for all clusters without having to predefine the number of clusters. Thus, it fits our objectives to produce document clusters having high cohesion, and to keep the minimum number of clusters. Moreover, an incremental learning process will perform after a new document is inserted into the tree, and the clusters will be rebuilt to accommodate the new information. In addition, we applied the proposed clustering algorithm to spam mail classification and the experimental results show that tree-based text clustering spam filter gives higher accuracy and specificity than the cobweb clustering, naïve Bayes and KNN.

Keywords: clustering; data mining; text clustering; text mining; text stream; tree-based clustering; spam; spam classification; text classification.

DOI: 10.1504/IJDMMM.2018.095354

International Journal of Data Mining, Modelling and Management, 2018 Vol.10 No.4, pp.353 - 370

Received: 20 Feb 2017
Accepted: 03 Jan 2018

Published online: 04 Sep 2018 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article