Cbica: Correlation based incremental clustering algorithm, a new approach

With progress in the area of computer science, it is achievable to read, process, store and generate information out of the available data. Humongous amount of data is generated, which is of mixed type, including time-series, Boolean, spatial-temporal and alpha-numeric data. This data is generated at a very giant speed and volume, which makes difficult for the traditional clustering algorithms to create and maintain the desired clusters. Thus, the proposed system encourages incremental clustering using a non-probability based similarity measure. The experimental results, of Correlation Based Incremental Clustering Algorithm (CBICA), which are obtained using the Pearson's coefficient of correlation, are compared with the experimental results of the Closeness-Factor Based Algorithm (CFBA), which uses the probability based similarity measures. The threshold computation is done to decide the cluster members in the post clustering phase, to adapt influx of new data. Wherein the new data is accommodated in the available clusters or new clusters are formed, depending upon the threshold values.