Document Clustering Using Keyword Extraction

Increase in the number of research documents on a daily basis, we find difficulty in identifying proper documents as per our requirements. This paper discusses an effective method in document clustering using automatic keyword extraction. Keyword is the smallest unit that can convey the meaning of an entire page, it helps a user in deciding whether or not to read or skip an article. In this work, we compare different methods of keyword extraction and choose the best method of keyword extraction based on accuracy and precision. The proposed approach takes extracted keywords as input and constructs a variety of different clusters using Euclidean distance measure to group the document together. As a result, a user can conduct a keyword search and obtain the results within seconds. The use of keyword clusters reduces noise in data and consequently enhances cluster quality.