… clustering and merging different clustering algorithms given the requirement of handling
sequential ,extensive data … Various clustering algorithms have been developed under different …

… , classification, prediction algorithms etc. However the use of these algorithms with educational
… the different types of clustering algorithms as applied in Educational Data Mining context. …

… of clustering algorithms to cope with big data challenges from very first proposed algorithms
… In this section advancements of clustering algorithms for big data analysis in categories that …

… it for the task of clustering data streams. In the future work, we will revise Squeezer to make
it more suitable for clustering data streams in the restricted data stream model. Automatic …

… value on peatland on DBSCAN Algorithm to clustering data on peatland hotspots in sumatera.
DBSCAN is a base algorithm for density based data clustering which contain noise and …

… data, that is, data mining clustering algorithms. Here we have studied and made a comparative
analysis of four classic clustering algorithms namely K-means, BIRCH, DBSCAN, STING. …

… (GAs) is applied to the clustering algorithm. GAs are the best-known … of clusters and to
provide appropriate clustering. This paper present some existing GA based clustering algorithms …

… In this paper, data clustering refers to the problem of dividing N data points into K groups so
… the cluster centers. Given a very large set of multidimensional data points, the data space is …

… way clustering handles uncertainty in terms of cluster overlapping. Fuzzy clustering, which
uses fuzzy techniques to cluster data … can be classified to more than one clusters. This type of …

… point and cluster’s centroid with this threshold distance through which we can minimize the
… between data point and cluster’s centroid. It is shown that how the modified k-mean algorithm …