Title :
Incorporating fuzzy clusters in semi-supervised text categorization
Author :
Wajeed, M.A. ; Adilakshmi, T.
Author_Institution :
SCSI, Sreenidhi Inst. of Sci. & Technol., Hyderabad, India
Abstract :
Today computing devices generate abundant information which has to be classified and stored to make navigation easier. Semi-supervised learning which is in-between supervised learning and unsupervised learning is explored is explored in the paper, incorporating the fuzziness in the process of textual classification is proposed. Semi-supervised classification is used where training data is not adequately available when compared with the supervised training. In the process of classification KNN (K - Nearest Neighbor) algorithm is applied.
Keywords :
fuzzy set theory; learning (artificial intelligence); pattern classification; pattern clustering; text analysis; KNN algorithm; fuzzy clustering; k-nearest neighbor algorithm; semi-supervised learning; semi-supervised text categorization; supervised learning; textual classification; unsupervised learning; Accuracy; Clustering algorithms; Equations; Support vector machine classification; Text categorization; Training; Training data; KNN; Semi-supervised learning; fuzzy classification; textual classification;
Conference_Titel :
Engineering (NUiCONE), 2011 Nirma University International Conference on
Conference_Location :
Ahmedabad, Gujarat
Print_ISBN :
978-1-4577-2169-4
DOI :
10.1109/NUiConE.2011.6153238
