---In big data, data visualization is an impressive concept to represent data for efficient data analysis to handle high dimensional data. In data visualization, there are three main properties i) to represent without loss of data patterns ii) without any changes in data pattern change the attributes iii) data visualization with structure and unstructured data attributes for data analysis. There are many types of data visualization are presented practically to define data analysis (i.e. topic based data visualization, attribute based data visualization, audio based data visualization and text based data visualization in different data sets). Parallel co-ordinate is an efficient and effective data visualization tool to analyze and handle multi attribute high dimensional data. It is based 5Ws density sending and receiving data visualization, it also read data patterns and attributes with reduces the overlapping to data patterns. Similarity measure is a categorization property to represent data with relationship objects in data set evaluation with different pair of attributes. We need to improve parallel coordinate tool to support multi-attribute object relations, so we propose and implement novel method i.e. (Similarity Measure Centered with Multi Viewpoint (SMCMV)) approach and related clustering approaches to represent data. Using multi-viewpoint, we can achieve assessment based similarity index with data visualization. Using multi viewpoint, we present theoretical analysis based on multi attributes presentation. Our experimental results gives best data representation in data visualization with efficient similarity measure on real time document evaluation with different known collected clustering approaches.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.