Temporal Cluster Graphs for Identifying and Visualizing Trends in Multi Attribute Transactional Data projects main idea is to implement a new data analysis and visualization procedures for improving trends in multi attribute temporal data using cluster based approach. In present scenario many organizations and business firms are more interested in capturing data of their customers, suppliers and information of business competitors to improve business. Most of the data that are used here is multi attribute and temporal in nature. One of the other technique used for capturing this data is using data mining techniques capturing this data is not a easy task. This paper explains about how clustering based approach can represent trends in multi attributed temporal data.
This application uses C-Trend to implement temporal cluster graphs which maps multi attribute temporal data in the form of two dimensional directed graph which identifies trends in domain data over time.
This system works on clustering based techniques, implementing new algorithms to increase performance