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ML-EC2: An Algorithm for Multi-Label Email Classification Using Clustering

ML-EC2: An Algorithm for Multi-Label Email Classification Using Clustering
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Author(s): Aakanksha Sharaff (National Institute of Technology Raipur, Raipur, India) and Naresh Kumar Nagwani (National Institute of Technology Raipur, Raipur, India)
Copyright: 2020
Volume: 15
Issue: 2
Pages: 15
Source title: International Journal of Web-Based Learning and Teaching Technologies (IJWLTT)
Editor(s)-in-Chief: Mahesh S. Raisinghani (Texas Woman's University, USA)
DOI: 10.4018/IJWLTT.2020040102

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Abstract

A multi-label variant of email classification named ML-EC2 (multi-label email classification using clustering) has been proposed in this work. ML-EC2 is a hybrid algorithm based on text clustering, text classification, frequent-term calculation (based on latent dirichlet allocation), and taxonomic term-mapping technique. It is an example of classification using text clustering technique. It studies the problem where each email cluster represents a single class label while it is associated with set of cluster labels. It is multi-label text-clustering-based classification algorithm in which an email cluster can be mapped to more than one email category when cluster label matches with more than one category term. The algorithm will be helpful when there is a vague idea of topic. The performance parameters Entropy and Davies-Bouldin Index are used to evaluate the designed algorithm.

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