Application of Community Detection Technique in Text Mining
The word community detection indicates the group of similar kinds of objects. In data mining techniques, this term is used as the cluster formation or data cluster analysis. The presented work is focused on evaluation of text for finding the similarity am
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1 Introduction The data mining is a technique that enables us to evaluate or analyze the data automatically. This ability of data mining offers to analyze the significant amount of data in less amount of human effort consumption [1]. In this work, the data mining technique is utilized for text mining. The text mining is a domain where the data mining algorithms and techniques are applied for analyzing the text data [2]. The text mining techniques includes the various phases of data analysis such as pre-processing, feature computation, implementation of data mining algorithm on feature set recovered, and finally the evaluation of performance on the basis of application [3]. In this context, various techniques of data mining techniques are developed that are claimed to provide the accurate and efficient data analysis. But the text data is not completely separable from each other; a partial similarity always exists among various subjects of data or different domains of data. Therefore in order to understand the similarity and the differences among two given text documents, they are needed to be evaluated. In addition to that the text is an unstructured kind of data which is not available in pre-labeled format. Thus, making accurate classification technique for the text is also a complicated task [4]. In order to deal with the considered issues and text mining challenges, a new approach of text mining is proposed in this work. That automatically analyzes the data and discovers the possible similar groups in data. This automatic recovery of S. Dubey (B) · A. Tiwari Information Technology, Mahakal Institute of Technology, Ujjain, India e-mail: [email protected] A. Tiwari e-mail: [email protected] J. Agrawal Computer Science and Engineering, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 R. K. Shukla et al. (eds.), Data, Engineering and Applications, https://doi.org/10.1007/978-981-13-6347-4_4
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data groups is termed here as the community detection in the text mining. In addition to that to make understandable the similarity in interclusters [5], the visualization technique is used. In addition to that sometimes the two clusters can share some kind of common data, thus it is also considered to evaluate the common amount and instances of data among multiple groups. This section provides the basic overview of the proposed work. The next section describes the complete system modeling and the phases of the system.
2 Proposed Work This section provides the detailed discussion and understanding about the proposed methodology. The methodology involves the solution development components, their functions, and the evaluation processes.
2.1 System Overview The proposed work is motivated to perform text mining using the new technique which promises to not only provide accurate cluster analysis but also provide the relativity among the available clusters on to another. Basically, the cluster technique is an unsupervise
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