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

DOI

10.2991/jsta.2018.17.3.5How to use a DOI?

Keywords

Time Dependent Information; Clustering; Mahalanobis Distance

Abstract

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.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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TY  - JOUR
AU  - Doo Young Kim
AU  - Chris P. Tsokos
PY  - 2018
DA  - 2018/09/30
TI  - Active and Dynamic Approaches for Clustering Time Dependent Information: Lag Target Time Series Clustering and Multi-Factor Time Series Clustering
JO  - Journal of Statistical Theory and Applications
SP  - 462
EP  - 477
VL  - 17
IS  - 3
SN  - 2214-1766
UR  - https://doi.org/10.2991/jsta.2018.17.3.5
DO  - 10.2991/jsta.2018.17.3.5
ID  - Kim2018
ER  -