Temporal Cluster graphs for visualizing Trends

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