TY - JOUR
T1 - C-TREND
T2 - Temporal cluster graphs for identifying and visualizing trends in multiattribute transactional data
AU - Adomavicius, Gediminas
AU - Bockstedt, Jesse
N1 - Funding Information:
The authors would like to thank the Digital Technology Center and the Carlson School of Management, University of Minnesota, for providing joint financial support of this research. The authors would also like to thank Prasad Sriram for his assistance with the development of the graphical user interface. Also, the research reported in this paper was supported in part by the US National Science Foundation CAREER Grant IIS-0546443.
PY - 2008/6
Y1 - 2008/6
N2 - 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.
AB - 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.
KW - Clustering
KW - Data and knowledge visualization
KW - Data mining
KW - Interactive data exploration and discovery
KW - Temporal data mining
KW - Trend analysis
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U2 - 10.1109/TKDE.2008.31
DO - 10.1109/TKDE.2008.31
M3 - Article
AN - SCOPUS:42949171875
SN - 1041-4347
VL - 20
SP - 721
EP - 735
JO - IEEE Transactions on Knowledge and Data Engineering
JF - IEEE Transactions on Knowledge and Data Engineering
IS - 6
M1 - 4445669
ER -