• Aranga Arivarasan
• Dr.M.Karthikeyan

The text documents are very important in the usage of www. Many users require so much text information to gather essential knowledge in their required field of interest. To serve the user internet the quite relevant required topic related documents are to be retrieved. To satisfy this requirement as well as indexing and retrieving the documents the researchers tend to produce many new algorithms in the field of text document mining. The proposed system carries out the problem by performing two different operations. Fist one is the feature extraction operation. The second on is the clustering operation. In the first operation to extract the features from the text document various operations like preprocessing, tokenization, Stop word removal, streaming and bag of Words were performed. By performing these operations the Document representing features namely WordCount, TF_IDF and probability of word occurrence were determined. The second operation is performed with K-means clustering algorithm. In the clustering phase the three features and some of the similarity measures were used to conclude the overall performance. The proposed method yields better performance for Spearman Similarity compared with other two Cosine Similarity and Correlation Similarity metrics.

TFIDF, Word Frequency, Probability, pre-processing, Clustering, K-Means

"Analysis of Similarity Metrics Through Clustering using WordCount,TF_IDF and Probability", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.168-179, May 2019, Available :http://www.jetir.org/papers/JETIRCY06027.pdf

"Analysis of Similarity Metrics Through Clustering using WordCount,TF_IDF and Probability", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp168-179, May 2019, Available at : http://www.jetir.org/papers/JETIRCY06027.pdf