An Enhanced Expectation Maximization Text Document Clustering Algorithm for E-Content Analysis

Abstract

Nowadays, there are many types of digital materials that can be used in the classroom. Students and scholars are migrating from textbooks to digital study materials because textbooks are too large and expensive. Teachers and college students can use and modify the materials that are freely available or with some constraints for their learning and teaching. E-content can be designed, evolved, utilized, re-used, and distributed electronically from anywhere at anytime. Because of the flexibility of time, place, and speed of learning, e-content is becoming extremely popular. It can be readily and instantly shared and communicated with an infinite number of clients all across the globe. Document clustering is most commonly used to group documents that are related to a specific topic. Text document clustering can be used to group a collection of documents regarding the information they include and to deliver search results when a user searches the internet. In this paper mainly focuses on text document clustering to cope with massive collection of E-Content documents. Enhanced Expectation Maximization Text Document Clustering (EEMTDC) clustering algorithm was proposed and compared with Expectation Maximization (EM) clustering, K-Means clustering, and Hierarchical clustering (HC) algorithms. The experiment shows that the performance of proposed EEMTDC algorithm produces greater clustering accuracy than existing clustering algorithms.