68 Int. J. Business Information Systems, Vol. 12, No. 1, 2013 Copyright © 2013 Inderscience Enterprises Ltd. Knowledge augmentation via incremental clustering: new technology for effective knowledge management Preeti Mulay* and Parag A. Kulkarni College of Engineering, Bharati Vidyapeeth University, Bharati Vidyapeeth Bhavan, L.B. Shastri Marg, Pune – 411 030, India E-mail: preetimilind@hotmail.com E-mail: paragakulkarni@yahoo.com *Corresponding author Abstract: Learning paradigm is associated with the study of how computers and natural systems such as humans learn in the presence of both labelled and unlabeled data. Traditionally, learning has been studied either in the unsupervised paradigm (e.g., clustering, outlier detection) where all the data are unlabeled or in the supervised paradigm (e.g., classification, regression) where all the data are labelled. ‘Incremental learning’ is an approach to deal with classification task or clustering when datasets are too large and when new information can arrive at any time, dynamically. We propose a new incremental clustering algorithm based on closeness, an efficient and scalable approach which updates cluster and learn new information effectually. Confusion matrix is implemented to validate the results given by proposed system as compared to published results. The proposed systems achieves knowledge augmentation, incremental learning via incremental clustering without compromising quality of data and saving computing time and complexity. Keywords: incremental learning; incremental clustering; knowledge augmentation. Reference to this paper should be made as follows: Mulay, P. and Kulkarni, P.A. (2013) ‘Knowledge augmentation via incremental clustering: new technology for effective knowledge management’, Int. J. Business Information Systems, Vol. 12, No. 1, pp.68–87. Biographical notes: Preeti Mulay is a research scholar and a PhD student in the area of software engineering. She completed her MS in Software Engineering from Wayne State University, MI, USA in 2002 and MTech in Software Engineering from JNTU, India in 2000. She is working in the education field since 1995 on various positions. Her areas of research include software engineering, pattern matching, forecasting, knowledge management, clustering, and machine learning. Dr. Parag A. Kulkarni is an alumnus of IIT and IIM. He completed his PhD in Computer Engineering from IIT Kharagpur. He has been working in IT industry for the last 17 years. He has worked as the Research Head, Operations Head, GM, Director and was instrumental in building world-class software product companies. He is working as Vice-President Strategic Development and Chief Scientist at Capsilon India. His name and profile is selected for listing in Marquis Who’s Who in the World (Science and Engineering) – 2009.