Elite fuzzy clustering ensemble based on clustering diversity and quality measures

In spite of some attempts at improving the quality of the clustering ensemble methods, it seems that little research has been devoted to the selection procedure within the fuzzy clustering ensemble. In addition, quality and local diversity of base-clusterings are two important factors in the selection of base-clusterings. Very few of the studies have considered these two factors together for selecting the best fuzzy base-clusterings in the ensemble. We propose a novel fuzzy clustering ensemble framework based on a new fuzzy diversity measure and a fuzzy quality measure to find the base-clusterings with the best performance. Diversity and quality are defined based on the fuzzy normalized mutual information between fuzzy base-clusterings. In our framework, the final clustering of selected base-clusterings is obtained by two types of consensus functions: (1) a fuzzy co-association matrix is constructed from the selected base-clusterings and then, a single traditional clustering such as hierarchical agglomerative clustering is applied as consensus function over the matrix to construct the final clustering. (2) a new graph based fuzzy consensus function. The time complexity of the proposed consensus function is linear in terms of the number of data-objects. Experimental results reveal the effectiveness of the proposed approach compared to the state-of-the-art methods in terms of evaluation criteria on various standard datasets. © 2018, Springer Science+Business Media, LLC, part of Springer Nature

Reliability-based fuzzy clustering ensemble

In the clustering ensemble the quality of base-clusterings influences the consensus clustering. Although some researches have been devoted to weighting the base-clustering, fuzzy cluster level weighting has been ignored, more specifically, they did not pay attention to the role of cluster reliability in the fuzzy clustering ensemble. In this paper, we propose a new fuzzy clustering ensemble framework without access to the features of data-objects based on fuzzy cluster-level weighting. The reliability of each fuzzy cluster is computed based on estimation of its unreliability, and is considered as its weight in the ensemble. The unreliability of fuzzy clusters is estimated by applying the similarity between fuzzy clusters in the ensemble based on an entropic criterion. In our framework, the final clustering is produced by two types of consensus functions: (1) a reliability-based weighted fuzzy co-association matrix is constructed from the base-clusterings and then, a single traditional clustering such as hierarchical agglomerative clustering or K-means is applied over the matrix to produce the final clustering. (2) a new graph based fuzzy consensuses function. The graph based consensus function has linear time complexity in the number of data-objects. Experimental results on various standard datasets demonstrated the effectiveness of the proposed approach compared to the state-of-the-art methods in terms of evaluation criteria and clustering robustness. © 2020 Elsevier B.V

An efficient earth magnetic field MEMS sensor: modelling and experimental results

The paper presents the experimental results and performance indexes of a new z-axis Lorentz force MEMS magnetometer with simple design, reduced dimensions and high efficiency. An ad-hoc formulated multi-physics approach is exploited to compute the sensor dynamics. Possible parasitic acceleration sensitivity is mechanically canceled in the proposed device. Optimality of the proposed configuration is discussed by means of an ad hoc formulated parameter optimization approach

An efficient earth magnetic field MEMS sensor: modeling, experimental results and optimization.

A new z-axis Lorentz force microelectromechanical systems magnetometer was designed, fabricated, and tested. The proposed device is characterized by simple design, reduced dimensions, and high efficiency. Furthermore, possible parasitic acceleration sensitivity is mechanically canceled in the proposed device. The initial design was subsequently studied through an ad hoc formulated multiphysics model used to compute the sensor dynamics; optimality of the design configuration was then obtained by means of a structural optimization approach. A wide scenario of design configurations, obtained with the proposed optimization approach, is finally discusse