C-TREND: Temporal Cluster Graphs for Identifying and Visualizing Trends in Multiattribute Transactional Data

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.

1 Introduction

Business intelligence applications represent an important opportunity for data mining techniques to help firms gather and analyze information about their performance, customers, competitors, and business environment. Knowledge representation and data visualization tools constitute one form of business intelligence techniques that present information to users in a manner that supports business decision-making processes. In this paper, we develop a new data analysis and visualization technique that presents complex multiattribute temporal data in a cohesive graphical manner by building on well-established data mining methods. Business intelligence tools gain their strength by supporting decision-makers, and our technique helps the users leverage their domain expertise to generate knowledge visualization diagrams from complex data and further customize them.