EconPapers    
Economics at your fingertips  
 

A Hybrid Validity Index to Determine K Parameter Value of k-Means Algorithm for Time Series Clustering

Fatma Ozge Ozkok and Mete Celik ()
Additional contact information
Fatma Ozge Ozkok: Department of Computer Engineering, Erciyes University, Kayseri, 38039, Turkey
Mete Celik: Department of Computer Engineering, Erciyes University, Kayseri, 38039, Turkey

International Journal of Information Technology & Decision Making (IJITDM), 2021, vol. 20, issue 06, 1615-1636

Abstract: Time series is a set of sequential data point in time order. The sizes and dimensions of the time series datasets are increasing day by day. Clustering is an unsupervised data mining technique that groups objects based on their similarities. It is used to analyze various datasets, such as finance, climate, and bioinformatics datasets. k-means is one of the most used clustering algorithms. However, it is challenging to determine the value of k parameter, which is the number of clusters. One of the most used methods to determine the number of clusters (such as k) is cluster validity indexes. Several internal and external validity indexes are used to find suitable cluster numbers based on characteristics of datasets. In this study, we propose a hybrid validity index to determine the value of k parameter of k-means algorithm. The proposed hybrid validity index comprises four internal validity indexes, such as Dunn, Silhouette, C index, and Davies–Bouldin indexes. The proposed method was applied to nine real-life finance and benchmarks time series datasets. The financial dataset was obtained from Yahoo Finance, consisting of daily closing data of stocks. The other eight benchmark datasets were obtained from UCR time series classification archive. Experimental results showed that the proposed hybrid validity index is promising for finding the suitable number of clusters with respect to the other indexes for clustering time-series datasets.

Keywords: Clustering; time series; k-means; automatic clustering; clustering validation index; hybrid validity index (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0219622021500449
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:ijitdm:v:20:y:2021:i:06:n:s0219622021500449

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0219622021500449

Access Statistics for this article

International Journal of Information Technology & Decision Making (IJITDM) is currently edited by Yong Shi

More articles in International Journal of Information Technology & Decision Making (IJITDM) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().

 
Page updated 2025-03-20
Handle: RePEc:wsi:ijitdm:v:20:y:2021:i:06:n:s0219622021500449