Author:
Muhammad Marwan Muhammad Fuad
Affiliation:
University of Tromsø, Norway
Keyword(s):
Data Mining, Differential Evolution, Distance Metrics, Fuzzy C-Means Clustering, Genetic Algorithms, Overfitting, Particle Swarm Optimization, Time Series.
Related
Ontology
Subjects/Areas/Topics:
Classification
;
Evolutionary Computation
;
Pattern Recognition
;
Theory and Methods
Abstract:
Fuzzy c-means clustering (FCM) is a clustering method which is based on the partial membership concept. As with the other clustering methods, FCM applies a distance to cluster the data. While the Euclidean distance is widely-used to perform the clustering task, other distances have been suggested in the literature. In this paper we study the use of a weighted combination of metrics in FCM clustering of time series where the weights in the combination are the outcome of an optimization process using differential evolution, genetic algorithms, and particle swarm optimization as optimizers. We show how the overfitting phenomenon interferes in the optimization process that the optimal results obtained during the training stage degrade during the testing stage as a result of overfitting.