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Abstract

In this paper specifically focus on time-varying volume data visualization and investigate transition relationships among data items over time. We present a mining approach that automatically extracts features from a graph-based representation for understanding time varying data. Beyond straightforward graph properties, users are given further guidance available through a series of graph analysis techniques including graph simplification, community detection, and visual recommendation. Graph simplification condenses a large graph to a smaller one by abstracting known structures, such as fan, connector, and clique, presenting a less cluttered view for quick comprehension of the overall graph structure. Community detection organizes nodes with close relationships into groups, allowing visual comparison between groups of nodes instead of individual nodes. Visual recommendation automatically highlights individual nodes or node groups based on user selected items, enabling users to spend more time on the actual analysis instead of painstaking interaction. In this paper plan to extend proposed work to handle multivariate data sets. We can either fuse multiple variables into one type of node, or construct one type of node for each individual variable and visualize the relations between variables using compound graphs. We will also investigate time-evolving graphs derived from scientific data sets for identifying temporal hotspots, detecting anomaly, and aligning multiple graphs for finding common features and distinct patterns. In this paper addition types of data sources are used for the recommendations, essentially these data sources can be modeled in the form of various types of graphs. This paper aims at providing a general framework on mining Web graphs for recommendations, 1) A novel diffusion method is proposed which propagates similarities between different nodes and generates recommendations 2) then it is illustrated how to generalize different recommendation problems into the graph diffusion framework.

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How to Cite
C.Navamani, & G.Visithra. (2019). Mining graphs for understanding time -varying volumetric data . International Journal of Intellectual Advancements and Research in Engineering Computations, 7(1), 876–884. Retrieved from https://ijiarec.com/ijiarec/article/view/1019