C-TREND: A New Technique for Identifying Trends in Transactional Data

The research field of data mining has developed sophisticated methods for identifying patterns in data in order to provide insights to users. Identifying temporal relationships (e.g., trends) in data constitutes an important problem that is relevant in many business and academic settings, and the data mining literature has provided analytical techniques for some specialized types of temporal data, e.g., time series analysis (Brockwell and Davis 2001, Keogh and Kasetty 2003, Roddick and Spiliopoulou 2002) and sequence analysis (Pei et al. 2004, Zaki 2001) techniques. Temporal data can take many forms, most commonly being general transactional (multi)attribute-value data, for which time series or sequence analysis methods are not particularly well suited.