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

作者：Aghabozorgi Saeed^*; Teh Ying Wah; Herawan Tutut; Jalab Hamid A; Shaygan Mohammad Amin; Jalali Alireza

来源：The Scientific World Journal, 2014, 2014: 562194.

DOI：10.1155/2014/562194

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.

• 出版日期2014