• Dr.Aranga.Arivarasan
• D.Kumaresan
• Dr. M. Natarajan

The text documents are very important in the usage environment of digital world. Numerous users have in need of too much text document to gather the information in their required field of interest. To serve the internet surfers the appropriate required topic documents are to be retrieved. For this purpose for indexing and retrieving the text document the researchers tend to produce many algorithms in the field of text document mining. The entire effort of clustering is achieved relying on the selection of appropriate similarity metrics. The proposed system builds the clustering operation by means of two segments of sequence of operations. The primary one is the operation of feature extraction from the document corpus. The next one is the clustering operation. In the initial process to extract the features from the text document several tasks like preprocessing, tokenization, Stop word removal, streaming and bag of Words were performed. Through the execution of extraction the Document representing features namely TF_IDF and probability of words were determined to perform the clustering operation with K-means clustering algorithm. In the clustering operation the two features and few of the similarity measures were used to perform the clustering operation. The proposed method yields better performance for Spearman Similarity compared with other two Cosine Similarity and Pearson Correlation Similarity metrics

–TFIDF, Probability, pre-processing, Clustering, K-Means

"TF_IDF AND PROBABILITY BASED CLUSTERING SCHEME FOR LARGE DENSE TEXT DOCUMENT ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 1, page no.b321-b329, January-2022, Available :http://www.jetir.org/papers/JETIR2201143.pdf

"TF_IDF AND PROBABILITY BASED CLUSTERING SCHEME FOR LARGE DENSE TEXT DOCUMENT ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 1, page no. ppb321-b329, January-2022, Available at : http://www.jetir.org/papers/JETIR2201143.pdf