Clustering Documents based on Semantic Similarity using HAC and K-Mean Algorithms
Karwan Jacksi, Rowaida Khalil Ibrahim, Subhi R. M. Zeebaree, Rizgar Ramadhan Zebari, Mohammed A. M. Sadeeq · 2020
The continuing success of the Internet has greatly increased the number of text documents in electronic formats. The techniques for grouping these documents into meaningful collections have become mission-critical. The traditional method of compiling documents based on statistical features and grouping did use syntactic rather than semantic. This article introduces a new method for grouping documents based on semantic similarity. This process is accomplished by identifying document summaries from Wikipedia and IMDB datasets, then deriving them using the NLTK dictionary. A vector space afterward is modeled with TFIDF, and the clustering is performed using the HAC and K-mean algorithms. The results are compared and visualized as an interactive webpage.