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The Earth is a system of numerous interconnected spheres, such as theclimate. Climate's global and regional influence requires understanding itsevolution in space and time to improve knowledge and forecasts. Analyzing andstudying decades of climate data is a data mining challenge. Cluster analysisminimizes data volumes and analyzes behavior by cluster. Understandinginvariant behavior is as crucial as understanding variable behavior. Griddeddata from two sources: Grided IMD data and CMIP5 HadCM3 decadal experiments,are studied using K-Means and MiSTIC clustering techniques to explorespatiotemporal clustering of maximum and minimum temperatures. The boundariesof k-means clustering correspond with topography. The Indian subcontinent'sphysiographic, climatic, and topographical characteristics affect MiSTIC's coreareas. Both techniques yield overlapping clusters. The datasets' MiSTIC clustercounts varied significantly. The impact of data on this technique is shown inhow the datasets group the Himalayas.

Spatiotemporal Cluster Analysis of Gridded Temperature Data -- A Comparison Between K-means and MiSTIC