C-TREND: Temporal cluster graphs for identifying and visualizing trends in multiattribute transactional data

Gediminas Adomavicius, Jesse Bockstedt

Research output: Contribution to journalArticle

21 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number4445669
Pages (from-to)721-735
Number of pages15
JournalIEEE Transactions on Knowledge and Data Engineering
Volume20
Issue number6
DOIs
StatePublished - Jun 1 2008

Keywords

  • Clustering
  • Data and knowledge visualization
  • Data mining
  • Interactive data exploration and discovery
  • Temporal data mining
  • Trend analysis

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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