Improving Text Search Process using Text Document Clustering Approach

Ruchika Mavis Daniel, Arun Kumar Shukla

Abstract: Knowledge discovery and data mining is a process of retrieving the meaningful knowledge from the raw data; using different techniques. Therefore; text mining is a sub domain of knowledge discovery from the text data. This paper provides a different way of understanding the text mining and their applications in different real time applications. This paper also includes the design of a hybrid text document clustering approach by which the document organization becomes easier for text search process. That includes the concept of fuzzy calculation; text summarization and traditional k-means clustering for cluster analysis of data. The implementation of the proposed methodology is summarized using a new kind of document clustering algorithm; and implemented using visual studio environment. After implementation of the proposed methodology the performance of the designed system is evaluated using the N-cross validation process. According to the obtained results the proposed system outperformed for text categorization with small amount of resources consumption. So; the proposed methodology is efficient and adoptable for different real time application.

Keywords: text search, text clustering, text classification and categorization

How to Cite?: Ruchika Mavis Daniel, Arun Kumar Shukla, "Improving Text Search Process using Text Document Clustering Approach", Volume 3 Issue 5, May 2014, International Journal of Science and Research (IJSR), Pages: 1424-1428, https://www.ijsr.net/getabstract.php?paperid=20132185, DOI: https://dx.doi.org/10.21275/20132185