Dear Colleagues,

The exponential growth of web technologies and data management tools has led to the possibility of accessing a large amount of data and the increasingly growing need to have data analysis and data mining methods suitable for dealing with massive data.

In order to explore large and very large datasets, it is necessary to experiment with innovative methods that guarantee an optimal performance while being unable to analyze the entire dataset as in traditional cases. Some variations of existing clustering algorithms based on data and dimension reduction techniques are proposed in the recent literature in order to deal with massive datasets.

Of considerable importance is designing efficient clustering algorithms to treat massive data for the fundamental role of cluster analysis in data and text mining and its applicability in many disciplines, such as image analysis, market analysis, spatial analysis, sentiment analysis, and bioinformatics.

This Special Issue on new trends in massive data clustering is aimed at industrial and academic researchers applying nontraditional clustering methods for handling massive data. The key areas of this Special Issue include but are not limited to:

• Kmeans and Fuzzy Cmeans variations for massive data;
• Fuzzy clustering for high dimensional data;
• Density-based and hybrid metaheuristic clustering algorithms;
• Big data clustering;
• Network modularity clustering;
• Cluster techniques for massive social media data streams;
• Cluster techniques for segmenting high resolution images.

Dr. Ferdinando Di Martino
Guest Editor

Manuscript Submission Information

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• massive datasets
• cluster algorithms
• fuzzy clustering form massive data
• big data clustering
• metaheuristic cluster algorithms for massive data
• cluster techniques for massive social media streams
• cluster techniques for high resolution image segmentation