A Hybrid Algorithm for Clustering of Time Series Data Based on Affinity Search Technique

Time series clustering is an important solution to various problems in numerous fields of research, including business, medical science, and finance. However, conventional clustering algorithms are not practical for time series data because they are essentially designed for static data. This impract...

Full description

Saved in:
Bibliographic Details
Published in:TheScientificWorld 2014-01, Vol.2014 (2014), p.1-12
Main Authors: Jalali, Alireza, Shayegan, Mohammad Amin, Jalab, Hamid A., Herawan, Tutut, Teh, Ying-Wah, Aghabozorgi, Said
Format: Article
Language:English
Subjects:
Algorithms
Cluster Analysis
Clustering (Computers)
Experiments
Methods
Pattern Recognition, Automated
Product development
Prototypes
Time series
Time-series analysis
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Time series clustering is an important solution to various problems in numerous fields of research, including business, medical science, and finance. However, conventional clustering algorithms are not practical for time series data because they are essentially designed for static data. This impracticality results in poor clustering accuracy in several systems. In this paper, a new hybrid clustering algorithm is proposed based on the similarity in shape of time series data. Time series data are first grouped as subclusters based on similarity in time. The subclusters are then merged using the k-Medoids algorithm based on similarity in shape. This model has two contributions: (1) it is more accurate than other conventional and hybrid approaches and (2) it determines the similarity in shape among time series data with a low complexity. To evaluate the accuracy of the proposed model, the model is tested extensively using syntactic and real-world time series datasets.
ISSN:2356-6140
1537-744X
1537-744X
DOI:10.1155/2014/562194