K-median clustering algorithms for time series data

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2021-3-10
Gökçem, Yiğit
Clustering is an unsupervised learning method, that groups the unlabeled data forgathering valuable information. Clustering can be applied on various types of data. Inthis study, we have focused on time series clustering. When the studies about timeseries clustering are reviewed in the literature, for the time series data, the centers ofthe formed clusters are selected from the existing time series samples in the clusters.In this study, we have changed that view and have proposed clustering algorithmsbased on the idea of selecting the cluster centers for each timestamp. With this view,we aim to improve the clustering performance. Based on this idea four differentalgorithms are suggested that are called as Center Based K-Median Algorithm (CKM),CKM with Haar Wavelet decomposition, CKM with Haar Wavelet DecompositionWithout Projection and Search Based CKM with Haar Wavelet Decomposition.In the first algorithm, the raw data is used and the clustering problem is solved by theproposed optimization model. The other three algorithms are also solved by using theproposed optimization model and instead of using raw data, transformed data, whichthe Haar wavelet decomposition is applied to, is used. The proposed algorithms havebeen experimented on different data sets and evaluated by using different internal and external indices. Due to the evaluations, successful results are obtained regardingclustering performances of the CKM based algorithms.

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Citation Formats
Y. Gökçem, “K-median clustering algorithms for time series data,” M.S. - Master of Science, Middle East Technical University, 2021.