A. H. Hadjahmadi, M. M. Homayounpour, S. M. Ahadi,
Volume 8, Issue 2 (6-2012)
Abstract
Nowadays, the Fuzzy C-Means method has become one of the most popular
clustering methods based on minimization of a criterion function. However, the
performance of this clustering algorithm may be significantly degraded in the presence of
noise. This paper presents a robust clustering algorithm called Bilateral Weighted Fuzzy CMeans
(BWFCM). We used a new objective function that uses some kinds of weights for
reducing the effect of noises in clustering. Experimental results using, two artificial
datasets, five real datasets, viz., Iris, Cancer, Wine, Glass and a speech corpus used in a
GMM-based speaker identification task show that compared to three well-known clustering
algorithms, namely, the Fuzzy Possibilistic C-Means, Credibilistic Fuzzy C-Means and
Density Weighted Fuzzy C-Means, our approach is less sensitive to outliers and noises and
has an acceptable computational complexity.
Pardis Asghari, Alireza Zakariazadeh,
Volume 19, Issue 4 (12-2023)
Abstract
This paper proposes a novel approach to analyzing and managing electricity consumption using a clustering algorithm and a high-accuracy classifier for smart meter data. The proposed method utilizes a multilayer perceptron neural network classifier optimized by an Imperialist Competitive Algorithm (ICA) called ICA-optimized MLP, and a CD Index based on Fuzzy c-means to optimally determine representative load curves. A case study involving a real dataset of residential smart meters is conducted to validate the effectiveness of the proposed method, and the results demonstrate that the ICA-optimized MLP method achieves an accuracy of 98.62%, outperforming other classification methods. This approach has the potential to improve energy efficiency and reduce costs in the power system, making it a promising solution for analyzing and managing electricity consumption.
