A New Incremental Semi-Supervised Graph Based Clustering

Incremental clustering or one-pass clustering is very useful when we work with data stream or dynamic data. In each incremental clustering algorithm, two process including insertion and deletion for new data points are used for updating the current clusters. In fact, for traditional clustering such as K-Means, Fuzzy C-Means, DBSCAN, etc., many versions of incremental clustering have been developed. However, to the best of our knowledge, there are no incremental semi-supervised clustering in literature. This paper introduces a new incremental semi-supervised clustering which was based on a graph of k-nearest neighbor using seeds, namely IncrementalSSGC. Experiments conducted on some data sets from UCI and the 802.11 network data set (AWID) show the effectiveness of our new IncrementalSSGC.