The set of objects having same characteristics are organized in groups and clusters of these objects are formed known as Data Clustering.It is an unsupervised learning technique for classification of data. K-means algorithm is widely used and famous algorithm for analysis of clusters.In this algorithm, n number of data points are divided into k clusters based on some similarity measurement criterion. K-Means Algorithm has fast speed and thus is used commonly clustering algorithm. Vector quantization,cluster analysis,feature learning are some of the application of K-Means.However results generated using this algorithm are mainly dependant on choosing initial cluster centroids.The main shortcome of this algorithm is to provide appropriate number of clusters.Provision of number of clusters before applying the algorithm is highly impractical and requires deep knowledge of clustering field. In this project, we are going to propose an algorithm for improvement in the initializing the centroids for K-Means algorithm. We are going to work on numerical data sets along with the categorical datasets with the n dimensions. For similarity measurement we are going to consider the manhattan distance ,Dice distance and cosine distance. The result of this proposed algorithm will be compared with the original K-Means.Also the quality and complexity of the proposed algorithm will be checked with the existing algorithm Index Terms-Data Clustering, K-Means,unsupervised learning,centroid.
The changes in land use/land cover can potentially impact the land surface temperature. The land-use features that are impenetrable can increase the Land Surface Temperature, while vegetation cover can decrease the Land Surface Temperature. In this study, we examined the Rajkot city in Saurashtra, Gujarat concerning changes in land use and temperature. Here, we used the imageries from Landsat for the years 2000, 2010, and 2020. We also analyzed the Land Surface Temperature to see how the changes in Land Use Land Cover influence the Land Surface Temperature of the city. We found that across three decades, the city's barren land and fallow land have been decreased and settlement, wetlands, vegetation cover, and industrial areas have been increased. Subsequently, the changes in Land Surface Temperature were noticed. The Land Surface Temperature decreased in the regions where barren land and fallow land are decreased while Land Surface Temperature increased in the areas where wetlands and vegetation cover increased. We conclude that the replacement of the barren and fallow land with human settlement and vegetation cover can decrease the Land Surface Temperature at the local scale.
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