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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:TheScientificWorld Ročník 2014; číslo 2014; s. 1 - 12
Hlavní autoři: Jalali, Alireza, Shayegan, Mohammad Amin, Jalab, Hamid A., Herawan, Tutut, Teh, Ying-Wah, Aghabozorgi, Said
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cairo, Egypt Hindawi Publishing Corporation 01.01.2014
John Wiley & Sons, Inc
Wiley
Témata:
Algorithms
Cluster Analysis
Clustering (Computers)
Experiments
Methods
Pattern Recognition, Automated
Product development
Prototypes
Time series
Time-series analysis
ISSN:2356-6140, 1537-744X, 1537-744X
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
Academic Editors: H. Chen, P. Ji, and Y. Zeng
ISSN:2356-6140
1537-744X
1537-744X
DOI:10.1155/2014/562194