A Literature Review on Text Document Clustering Algorithms used in Text Mining

U. S. Patki, Dr. P. G. Khot

Abstract


An Exhaustive growth of Knowledge in the form of textual documents in almost every area of digital era needs an extensive demand for new powerful tools to filter the text documents and extract required knowledge from it. A research technology Known as ‘Text mining’ helps to discover required knowledge from a collection of text documents and design a system to provide this knowledge to support the user’s decision. The text miner program gathers the relevant documents (Textual data) together, mines the information and converts this unstructured data into structured database. Document Clustering plays an important role in Text Mining. A clustering is defined as a grouping of documents including features, which are more similar to each other than to the features of any other group. In other words, documents from one cluster share some common features, which distinguish them from the other documents. This paper gives a literature survey on different document clustering techniques. The paper briefly studies hard clustering techniques and tries to explore soft computing techniques in detail.


Keywords


Hard Computing, Soft Computing, FCM, PCM, FPCM

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References


Ashish Jaiswal & Prof. Nitin Janwe (IJCA) 2011 “Hierarchical Document Clustering: A Review”

Tapas Kanungo, Senior Member, IEEE, et al (2002) “An Efficient k-Means Clustering Algorithm: Analysis and Implementation”

Mrs Sanjivani Tushar Deokar [IJTES] , July 2013 “Text Documents clustering using K Means Algorithm”

Nidhi Grover, IJER 2014 “A study of various Fuzzy Clustering Algorithms”

Ms.K.Sruthi and Mr.B.Venkateshwar Reddy, 2013,

“Document Clustering on Various Similarity Measures”

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