Analysis on Spatial Data Clustering Methods - A Case Study
The recent advancements and cost reduction technologies for collecting spatial data like satellite images, cellular phones, sensor networks, and GPS devices has facilitated huge collection of data referenced in space and time. The conventional systems and classical data mining techniques are not useful in discovering or retrieving the interesting hidden information from these large collections of data. Spatial data are embedded in continuous space, whereas classical datasets (e.g. transactions) are often discrete. Spatial data require complex data preprocessing, transformation, data mining, and post-processing techniques to extract novel, useful, and understandable desired patterns.