Active and Dynamic Approaches for Clustering Time Dependent Information: Lag Target Time Series Clustering and Multi-Factor Time Series Clustering

• Published in: Journal of statistical theory and applications Vol. 17; no. 3; pp. 462 - 477
• Main Authors: Kim, Doo Young, Tsokos, Chris P.
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.09.2018; Springer Nature B.V; Springer
• Subjects: Clustering; Data mining; Mahalanobis Distance; Research Article; Time dependence; Time Dependent Information; Time series; Clustering; Mahalanobis Distance; Time Dependent Information
• ISSN: 1538-7887; 2214-1766; 1538-7887
• DOI: 10.2991/jsta.2018.17.3.5

One of data mining schemes in statistics is clustering panel data such as longitudinal data and time series data. Classical approaches to cluster such time dependent information do not properly count time dependencies among objects we are interested to analyze. In the present study, we propose an approach which takes time dependencies into our consideration by introducing appropriate weight factors with an add-on approach which allows us to measure pairwise distances in multi-dimensional space not just in two dimension. We refer to these approaches LTTC (Lag Target Time Series Clustering) and MFTC (Multi-Factor Time Series Clustering) , respectively. These proposed methods in the present study are applicable to any time dependent information from various research areas, and we have applied these methods to state level brain cancer mortality rates in the United States that illustrates the importance of subject methods.