Temporal Cluster graphs for visualizing Trends

Abstract

Organizations and firms are capturing increasingly more data about their customers, suppliers,<lb>competitors and business environment. Most of this data is multidimensional and temporal in<lb>nature. Data mining and business intelligence technique are often used to discover in such<lb>data. We propose a new data analysis and visualization technique for representing trends and<lb>temporal data using K-means clustering based approach. And we introduce a system that<lb>implements the temporal clustered graph construct which maps temporal data to a two<lb>dimensional directed graph that identifies trends in dominant data types over time. In this<lb>paper, we present our temporal clustered based technique and its implementation and<lb>performance.<lb>References

Cite this paper

@inproceedings{Ratnamala2012TemporalCG, title={Temporal Cluster graphs for visualizing Trends}, author={B. Ratnamala and Pooja M Kiran and Ruchit Agrawal and Katherine I-Chun Lin and Harpreet S. Sawhney and Cl{\'a}udia Antunes and Margaret H. Dunham}, year={2012} }