Abstract
With the expeditious growth of unstructured massive data on the World Wide Web (WWW), more advanced tools, techniques, methods, and approaches for information organization and retrieval are desired. Text mining is one such approach to achieve the above mentioned demand. One of the main text mining applications is how to classify data presented by different industries into groups. In this paper, the classification of data into various groups based on the choice of the users at any given point of time is proposed. Here, a support vector machine (SVM) based classification algorithm is established to classify the text data into two broad categories of Manufacturing and Non-Manufacturing suppliers. Later, the performance of the proposed classifier was tested experimentally using most commonly used accuracy measures such as precision, recall, and F-measure. Results proved the efficiency of the proposed approach for classification of the texts.
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Acknowledgment
This work has been supported by Department of Science and Technology, Science & Engineering Research Board (SERB), Statutory Body Established through an Act of Parliament: SERB Act 2008, Government of India with Sanction Order No ECR/2016/001808.
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Manupati, V.K., Akhtar, M.D., Varela, M.L.R., Putnik, G.D., Trojanowska, J., Machado, J. (2018). A Text Mining Based Supervised Learning Algorithm for Classification of Manufacturing Suppliers. In: Rocha, Á., Adeli, H., Reis, L., Costanzo, S. (eds) Trends and Advances in Information Systems and Technologies. WorldCIST'18 2018. Advances in Intelligent Systems and Computing, vol 747. Springer, Cham. https://doi.org/10.1007/978-3-319-77700-9_24
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DOI: https://doi.org/10.1007/978-3-319-77700-9_24
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