Data visualization is used to transforms data to image. The data are changed into image format for more understandability of data. Giving a huge dataset for the essential task of visualization is to visualize the data to tell compelling stories by selecting, filtering and transforming the data. It also used to pick the right visualization type such as bar charts or line charts. The ultimate task is to provide more visually effective data representation. A revolutionized system in the field faces the following three main challenges and they are Visualization verification, which it used to determine whether the visualization for a given dataset is interesting, from the viewpoint of human understanding. Visualization search space checks whether the resultant visualization is a “boring" dataset, then it may become interesting after an arbitrary combination of operations such as selections, joins, and aggregations, among others or not. On-time responses is does not deplete the user’s patience. The proposed system solves the above challenge by implementing multidimensional scaling or the popular t-SNE algorithm. The t-SNE algorithm is based on non-convex optimization, has become the standard for visualization in a wide range of applications. This work gives a formal framework for the problem of data visualization – finding a 2- dimensional embedding of cluster data that correctly separates individual clusters to make them visually identifiable. Ground-truth cluster is checks the conditions assumed in earlier analyses of clustering while underlying the data. To achieve the goal of data visualization existing system used LambdaMART algorithm to learn rank technique. In proposed system the t-SNE algorithm takes place the role to create more effective visualization with the help of clustering method to group or ungrouped huge data.

Machine learning, Computer Science, Artificial, Architecture and Syatems

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