Clustering algorithms for spatial big data

In our time people and devices constantly generate data. User activity generates data about needs and preferences as well as the quality of their experiences in different ways: i.e. streaming a video, looking at the news, searching for a restaurant or a an hotel, playing a game with others, making purchases, driving a car. Even when people put their devices in their pockets, the network is generating location and other data that keeps services running and ready to use. This rapid developments in the availability and access to data and in particular spatially referenced data in a different areas, has induced the need for better analysis techniques to understand the various phenomena. Spatial clustering algorithms, which groups similar spatial objects into classes, can be used for the identification of areas sharing common characteristics. The aim of this paper is to analyze the performance of three different clustering algorithms i.e. the Density-Based Spatial Clustering of Applications with Noise algorithm (DBSCAN), the Fast Search by Density Peak (FSDP) algorithm and the classic K-means algorithm (K-Means) as regards the analysis of spatial big data. We propose a modification of the FSDP algorithm in order to improve its efficiency in large databases. The applications concern both synthetic data sets and satellite images.