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Activity Number: 185 - SPEED: Environmental Statistics
Type: Contributed
Date/Time: Monday, July 31, 2017 : 11:35 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #325355
Title: Constrained Functional Clustering of Arctic Sea Ice Extent Data
Author(s): Tan Tran* and Christopher Barbour and Mark Greenwood
Companies: Montana State University and Montana State University and Montana State University
Keywords: monothetic clustering ; arctic sea ; functional data ; constrained clustering ; classification
Abstract:

The Arctic Sea ice extent and area has been recorded since November 1978 until today by the National Snow & Ice Data Center and is available to the public as nearly daily data. There are studies looking at the data set as either time series data or as uncorrelated measurements of each day of the year. In this research, we extend this view by considering the ice extent area data as a functional data with functions estimated for each year. Of potential interest in this type of application is to be able to classify the curves based on the results from just a portion of the curve, say the first few months of the year. Two modifications of monothetic clustering, a type of clustering that creates clusters that share common characteristics, are proposed that only use information from early in each year to cluster the curves are explored. To illustrate the methods, the sea ice data from November 1978 to December 2014 are used as training data with the results from 2015 and 2016 used to test the classification performance of the methods.


Authors who are presenting talks have a * after their name.

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