Research Area:  Machine Learning

Abstract:

Clustering is a branch of data mining which involves grouping similar data in a collection known as cluster. Clustering can be used in many fields, one of the important applications is the intelligent text clustering. Text clustering in traditional algorithms was collecting documents based on keyword matching, this means that the documents were clustered without having any descriptive notions. Hence, non-similar documents were collected in the same cluster. The key solution for this problem is to cluster documents based on semantic similarity, where the documents are clustered based on the meaning and not keywords. In this research, fifty papers which use semantic similarity in different fields have been reviewed, thirteen of them that are using semantic similarity based on document clustering in five recent years have been selected for a deep study. A comprehensive literature review for all the selected papers is stated. A comparison regarding their algorithms, used tools, and evaluation methods is given. Finally, an intensive discussion comparing the works is presented.

Keywords:  
Clustering
data mining
intelligent text clustering
document
descriptive notions
semantic similarity

Author(s) Name:  Rowaida Khalil Ibrahim, Subhi Rafeeq Mohammed Zeebaree, Karwan Fahmi Sami Jacksi

Publisher name:  ASTES

DOI:  10.25046/aj040515

Volume Information:  Volume 4, Issue 5, Page No 115-122, 2019

Paper Link:   https://www.astesj.com/v04/i05/p15/