Active and Dynamic Approaches for Clustering Time Dependent Information: Lag Target Time Series Clustering and Multi-Factor Time Series Clustering
Document Type
Article
Publication Date
9-2018
Keywords
Time Dependent Information, Clustering, Mahalanobis Distance
Digital Object Identifier (DOI)
https://doi.org/10.2991/jsta.2018.17.3.5
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.
Rights Information
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
Journal of Statistical Theory and Applications, v. 17, issue 3, p. 462-477
Scholar Commons Citation
Kim, Doo Young and Tsokos, Chris P., "Active and Dynamic Approaches for Clustering Time Dependent Information: Lag Target Time Series Clustering and Multi-Factor Time Series Clustering" (2018). Mathematics and Statistics Faculty Publications. 21.
https://digitalcommons.usf.edu/mth_facpub/21
