A novel approach: big data analysis based on multi-view data visualization using clustering similarity measure - Rao Madala, Srinivasa; N. Rajavarman, V.; Venkata Satya Vivek, T. (International Journal of Engineering and Technology, 2018)
In big data, data visualization is annotable concept to represent data for competent data analysis to handle high dimensional data. In data visualization, there are three main properties i) to characterize without loss of data patterns ii) without any changes in data pattern change the attributes iii) data visualization among structure and unstructured data attributes for data examination. There are various types of data visualization are existing virtually to identify data analysis (i.e. topic based data revelation, attribute based data visualization, audio based data visualization and text based data visualization in different data sets). Parallel coordinate is Â proficient 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. Parallel measure is a labeling property to characterize data with affiliation objects in data set appraisal with different pair of attributes. We need to get better parallel coordinate tool to sustain multi-attribute object relations, so we recommend 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 accomplish assessment based similarity index with data visualization. Using multi viewpoint, we present hypothetical analysis based on multi attributes presentation. Our experimental results gives best data representation in data visualization with capable similarity measure on real time document evaluation with different known collected clustering approaches.Â Â