Abstract-Clustering is a pivotal building block in many data mining applications and in machine learning in general. Most clustering algorithms in the literature pertain to off-line (or batch) processing, in which the clustering process repeatedly sweeps through a set of data samples in an attempt to capture its underlying structure in a compact and ef cient way. However, many recent applications require that the clustering algorithm be online, or incremental, in the that there is no a priori set of samples to process but rather samples are provided one iteration at a time. Accordingly, the clustering algorithm is expected to gradually improve its prototype (or centroid) constructs. Several problems emerge in this context, particularly relating to the stability of the process and its speed of convergence. In this paper, we present a fast and stable incremental clustering algorithm, which is computationally modest and imposes minimal memory requirements. Simulation results clearly demonstrate the advantages of the proposed framework in a variety of practical scenarios.
A fast and stable incremental clustering algorithm use computationally modest and imposes minimal memory requirements REF The root mean square errors of the superensemble precipitation indicating a skilled forecast.
A Fast and Stable Incremental Clustering Algorithm
in 2010 Seventh International Conference on Information Technology. IEEE
Computer Science