A Query Language and Its Processing for Time-Series Document Clusters
ICADL 08 Proceedings of the 11th International Conference on Asian Digital Libraries: Universal and Ubiquitous Access to Information
Ranking of evolving stories through meta-aggregation
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Spatio-temporal clustering of road network data
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part I
Contrasting temporal trend discovery for large healthcare databases
Computer Methods and Programs in Biomedicine
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Organizations and firms are capturing increasingly 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. We propose a new data analysis and visualization technique for representing trends in multi-attribute temporal data using a clustering-based approach. We introduce C-TREND, a system that implements the temporal cluster graph construct, which maps multi-attribute temporal data to a two-dimensional directed graph that identifies trends in dominant data types over time. In this paper, we present our temporal clustering-based technique, discuss its algorithmic implementation and performance, demonstrate applications of the technique by analyzing data on wireless networking technologies and baseball batting statistics, and introduce a set of metrics for further analysis of discovered trends.