Knowledge augmentation via incremental clustering: new technology for effective knowledge management

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.