Semantic-Based K-Means Clustering for IMDB Top 100 Movies

Textual documents are growing rapidly through the internet in today’s modern technology era. Electronic structured databases archive offline and online documents, e-mails, webpages, blog social network posts. Without appropriate ranking demand clustering when there is classification without any specifics, it quite difficult to retain access these documents. K-means one of methods that frequently used for clustering. In terms determining proximity meaning or semantics between data, distance-based method still has flaws. To get around this issue, semantic similarity can be estimated by measuring level objects a cluster. This research provides based on similarity. The approach carried out defining document synopses from IMDB Wikipedia using NLTK dictionary, we provide semantic-based assesses not only data represented as vector space model with TFIDF, but also Precision, recall, F-measure, demonstrate how well technique works experimental findings top 100 movies datasets.