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In this paper, a Quantum-Inspired Evolutionary Fuzzy C-Means (QIE-FCM) algorithm is proposed. The proposed approach find the true number of clusters and the appropriate value of weighted exponent (m) which is required to be known in advance to perform clustering using Fuzzy C-Means (FCM) algorithm. However, the selection of inappropriate value of m and C may lead the algorithm to converge to the local...
In this paper, a multiobjective evolutionary algorithm based soft subspace clustering, MOSSC, is proposed to simultaneously optimize the weighting within-cluster compactness and weighting between-cluster separation incorporated within two different clustering validity criteria. The main advantage of MOSSC lies in the fact that it effectively integrates the merits of soft subspace clustering and the...
Detecting community structure is crucial for uncovering the links between structures and functions in complex networks. Most contemporary community detection algorithms employ single optimization criteria (e.g., modularity), which may have fundamental disadvantages. This paper considers the community detection process as a Multi-Objective optimization Problem (MOP). Correspondingly, a special Multi-Objective...
The applications of recently developed meta-heuristics in cluster analysis, such as particle swarm optimization (PSO) and differential evolution (DE), have increasingly attracted attention and popularity in a wide variety of communities owing to their effectiveness in solving complicated combinatorial optimization problems. Here, we propose to use a hybrid of PSO and DE, known as differential evolution...
While data clustering has a long history and a large amount of research has been devoted to the development of clustering algorithms, significant challenges still remain. One of the most important challenges in the field is dealing with high dimensional datasets. The class of clustering algorithms that utilises information from Principal Component Analysis has proven very successful in such datasets...
The K-means clustering is commonly used in applications of unsupervised classification and the related area due to its simplicity and effectiveness. In this study, an intelligent evolutionary K-means algorithm (IEKA) is firstly developed to optimize the cluster centers by using an improved real-coded genetic algorithm. Then, four cluster validation indices for data clustering are evaluated on six...
Clustering is a difficult problem, both with respect to the construction of adequate objective functions as well as to the optimization of the objective functions. In this article, the weighted sum validity function (WSVF) is improved as a dynamic weighted sum validity function(DWSVF) to evaluate fuzzy partitioning. Moreover, we proposed an adaptive differential evolution algorithm, which can be used...
K-harmonic means clustering algorithm (KHM) is a center-based like K-means (KM), which uses the harmonic averages of the distances from each data point to the centers as components to its performance function and overcomes KM's one major drawback that is highly dependent on the initial identification of elements that represent the clusters. However, KHM is also easily trapped in local optima. In this...
Although all three conventional c-means clustering algorithms, namely hard c-means (HCM), fuzzy c-means (FCM), and possibilistic c-means (PCM), had their merits in the development of clustering theory, none of them are generally good solutions for unsupervised classification. Several hybrid solutions have been proposed to produce mixture algorithms. Possibilistic-fuzzy hybrids generally attempt to...
The purpose of this paper is to propose new clustering technique on manifolds. This is achieved mainly with the help of tangent spaces that are determined by manifold learning. We embed a new searching algorithm based on differential evolution (DE). We present a simple convergence analysis with a design of experimental framework.
In order to comprehend the advantages and short-comings of each model-building algorithm they should be tested under similar conditions and isolated from the MOEDA it takes part of. In this work we will assess some of the main machine learning algorithms used or suitable for model-building in a controlled environment and under equal conditions. They are analyzed in terms of solution accuracy and computational...
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