Incremental Mitosis: Discovering Clusters of Arbitrary Shapes and Densities in Dynamic Data

While finding natural clusters in high dimensional data is in itself a challenge, the dynamic nature of data adds another greater challenge. Many applications such as Data Warehouses and WWW demand the presence of efficient incremental clustering algorithms to handle their dynamic data. So far, numerous useful incremental clustering algorithms have been developed for large datasets such as incremental K-means, incremental DBSCAN, similarity histogram-based clustering (SHC) and mean shift. However, targeting clusters of different shapes and densities is yet to be efficiently tackled. In this work, an efficient incremental clustering algorithm (Incremental Mitosis) is proposed. It is based on Mitosis clustering algorithm which maximizes the relatedness of distances between patterns of the same cluster. The proposed algorithm is able to discover clusters of arbitrary shapes and densities in dynamic high dimensional data. Experimental results show that the proposed algorithm efficiently clusters the data and maintains the accuracy of Mitosis algorithm.