Welcome to the InfoSci Platform
Reference Hub17
ML-EC2: An Algorithm for Multi-Label Email Classification Using Clustering

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

Aakanksha Sharaff, Naresh Kumar Nagwani
Copyright: © 2020 |Volume: 15 |Issue: 2 |Pages: 15
ISSN: 1548-1093|EISSN: 1548-1107|EISBN13: 9781799803973|DOI: 10.4018/IJWLTT.2020040102
Cite Article Cite Article

MLA

Sharaff, Aakanksha, and Naresh Kumar Nagwani. "ML-EC2: An Algorithm for Multi-Label Email Classification Using Clustering." IJWLTT vol.15, no.2 2020: pp.19-33. http://doi.org/10.4018/IJWLTT.2020040102

APA

Sharaff, A. & Nagwani, N. K. (2020). ML-EC2: An Algorithm for Multi-Label Email Classification Using Clustering. International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), 15(2), 19-33. http://doi.org/10.4018/IJWLTT.2020040102

Chicago

Sharaff, Aakanksha, and Naresh Kumar Nagwani. "ML-EC2: An Algorithm for Multi-Label Email Classification Using Clustering," International Journal of Web-Based Learning and Teaching Technologies (IJWLTT) 15, no.2: 19-33. http://doi.org/10.4018/IJWLTT.2020040102

Export Reference

Mendeley
Favorite Full-Issue Download

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.