Text Categorization Using Activation Based Term Set
Text classification is a challenging field in the current scenario and has great importance in text categorization application. Documents may be classified or categorized according to their subjects or according to their attributes. There is need to categorize a collection of text document into mutually exclusive categories by extracting the concept or features using supervised learning paradigm and different classification algorithm. In this paper we present a nave based approach for the classification using semi-supervised text classification methodology with the help of Activation term sets. Such frequent term set can be discovered based on David Merrills First principles of instruction (FPI) techniques. The system uses a pre-defined category group by providing them with the proper training set based on the activation of FPI We made an attempt to classify the document using FPI methodology, the algorithm involves the text tokenization, text categorization and text analysis
Keywords: Text mining, Text characterization, Text Classification, Text tokenization, FPI and Instructional phase
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ABOUT THE AUTHORS
M. Pushpa
M. Pushpa is pursuing PhD in computer science at Bharathiyar University, Coimbatore, Tamil Nadu, India. She is currently working as an Assistant professor in a reputed institution in India. Her area of interest is Artificial Intelligence, Data mining and Software Engineering.
K. Nirmala
K. Nirmala received her PhD degree in computer science from the university of Madras. She is at present an Associate Professor in the Department of Computer Science at Quaid-E-Millath Government College for Women in Chennai, Tamil Nadu, India. She has authored and co-authored many papers in an international Journal and international conferences
M. Pushpa
M. Pushpa is pursuing PhD in computer science at Bharathiyar University, Coimbatore, Tamil Nadu, India. She is currently working as an Assistant professor in a reputed institution in India. Her area of interest is Artificial Intelligence, Data mining and Software Engineering.
K. Nirmala
K. Nirmala received her PhD degree in computer science from the university of Madras. She is at present an Associate Professor in the Department of Computer Science at Quaid-E-Millath Government College for Women in Chennai, Tamil Nadu, India. She has authored and co-authored many papers in an international Journal and international conferences
