C-TREND: A New Technique for Indentifying and Visualizing Trends in Multi-Attribute Transactional Data

Organizations and firms are increasingly capturing more data about their customers, suppliers, competitors, and business environment. Most of this data is multi-attribute (multi-dimensional) and temporal in nature. Data mining and business intelligence techniques are often used to discover patterns in such data; however, mining temporal relationships typically is a complex task. The authors propose a new data analysis and visualization technique for representing trends in multi-attribute temporal data using a clustering-based approach. They introduce C-TREND, a system that implements the temporal cluster graph construct, which maps multi-attribute temporal data to a two-dimensional graph that clearly identifies trends in dominant data types over time.