4 research outputs found

    The Suitable Distance Function for Fuzzy C-Means Clustering

    Get PDF
    Fuzzy C-Means clustering is a form of clustering based on distance which apply the concept of fuzzy logic. The clustering process works simultaneously with the iteration process to minimize the objective function. This objective function is the summation from the multiplication of the distance between the data coordinates to the nearest cluster centroid with the degree of which the data belong to the cluster itself. Based on the objective function equation, the value of the objective function will decrease by increasing the number of iteration process. This research provide how we choose the suitable distance for Fuzzy C-Means clustering. The right distance will meet the optimization problem in the Fuzzy CMeans Clustering method and produce good cluster quality. They are Euclidean, Average, Manhattan, Chebisev, Minkowski, Minkowski-Chebisev, and Canberra distance. We use five UCI Machine Learning dataset and two random datasets. We use the Lagrange multiplier method for the optimization of this method. The result quality of the cluster measure by their accuracy, Davies Bouldin Index, purity, and adjusted rand index. The experiment shows that the Canbera distances are the best distances which provide the optimum result by producing minimum objective function 378.185. The suitable distance for the application of the Fuzzy C-Means Clustering method are Euclidean distance, Average distance, Manhattan distance, Minkowski distance, Minkowski-Chebisev distance, and Canberra distance. These six distances produce a numerical simulation that derives the objective function fairly constant. Meanwhile, the Chebisev distance shows the movement of the value of the objective function that fluctuates, so it is not in accordance with the optimization problem in the Fuzzy C Means Clustering method

    Optimal Design of Passive and Active Control Systems in Seismic-excited Structures Using a New Modified TLBO

    Get PDF
    Vibration control devices have recently been used in structures subjected to wind and earthquake excitations. The optimal design problems of the passive control device and the feedback gain matrix of the controller for the seismic-excited structures are some attractive problems for researches to develop optimization algorithms with the advancement in terms of simplicity, accuracy, speed, and efficacy. In this paper, a new modified teaching–learning-based optimization (TLBO) algorithm, known as MTLBO, is proposed for the problems. For some benchmark optimization functions and constrained engineering problems, the validity, efficacy, and reliability of the MTLBO are firstly assessed and compared to other optimization algorithms in the literature. The undertaken statistical indicate that the MTLBO performs better and reliable than some other algorithms studied here. The performance of the MTLBO will then be explored for two passive and active structural control problems. It is concluded that the MTLBO algorithm is capable of giving better results than conventional TLBO. Hence, its utilization as a simple, fast, and powerful optimization tool to solve particular engineering optimization problems is recommended

    A hybrid elicit teaching learning based optimization with fuzzy c-means (ETLBO-FCM) algorithm for data clustering

    No full text
    Since its inception, Fuzzy c-means (FCM) technique has been widely used in data clustering. The advantages of FCM such as balancing of individual number of cluster points, drifting of small cluster centers to large neighboring cluster centers, and presence of fuzzy factor, make it more popular. However, early trapping at local minima and high sensitivity to the cluster center initialization are the major limitations of FCM. In this paper, a novel Elicit Teaching learning based optimization (ETLBO) approach has been incorporated with the Fuzzy c-means clustering algorithm to obtain the improved fitness values of the cluster centers. The simulation results of the proposed method have been compared with some other existing methods such as GA, PSO and IPSO. Experimental results show that the proposed approach is superior to the other methods in terms of their fitness value calculations. Keywords: FCM, K-means, Elicit TLBO, TLBO, PSO, IPS
    corecore