Fuzzy Clustering of Series Using Quantile Autocovariances

Unlike conventional clustering, fuzzy cluster analysis allows data elements to belong to more than one cluster by assigning membership degrees of each data to clusters. This work proposes a fuzzy C– medoids algorithm to cluster time series based on comparing their estimated quantile autocovariance functions. The behaviour of the proposed algorithm is studied on different simulated scenarios and its effectiveness is concluded by comparison with alternative approaches.

• Type: Journal
• Year: 2015
• Where: PKDD
• Authors: Borja R. Lafuente-Rego, José Antonio Vilar
• Keywords: Data Mining, PKDD 2015