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A Text Mining Based Supervised Learning Algorithm for Classification of Manufacturing Suppliers


[ 1 ] Katedra Zarządzania i Inżynierii Produkcji, Wydział Budowy Maszyn i Zarządzania, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[2.9] Mechanical engineering

Year of publication


Chapter type

chapter in monograph / paper

Publication language


  • text mining
  • support vector machine
  • classification algorithm

EN 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.

Date of online publication


Pages (from - to)

236 - 244





Trends and Advances in Information Systems and Technologies : Volume 3

Presented on

World Conference on Information Systems and Technologies WorldCIST'18, 27-29.03.2018, Naples, Italy

Ministry points / chapter


Ministry points / conference (CORE)


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