Strategies for Big Data Clustering
Olga Kurasova, Virginijus Marcinkevičius, Viktor Medvedev, Aurimas Rapečka, Pavel Stefanovič · 2014
In the paper, an overview of methods and technologies used for big data clustering is presented. The clustering is one of the important data mining issue especially for big data analysis, where large volume data should be grouped. Here some clustering methods are described, great attention is paid to the k-means method and its modifications, because it still remains one of the popular methods and is implemented in innovative technologies for big data analysis. Neural network-based self-organizing maps and their extensions for big data clustering are reviewed, too. Some strategies for big data clustering are also presented and discussed. It is shown the data of which volume can be clustered in the well known data mining systems WEKA and KNIME and when new sophisticated technologies are needed.