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Clustering is one of the useful methods in many scientific fields. It is a classification process to group data in specific clusters based on their similarities. Many heuristic and meta-heuristic algorithms have been successfully applied in the literature to solve clustering problems. Among them, the K-means is one of the best due to its simplicity and computational efficiency. However, it suffers from several drawbacks, the most significant of which is its dependency on the initial state that leads to trapping in local optima. In this paper, the K-means method is combined with a modified electromagnetism-like mechanism (MEM) algorithm to develop a new algorithm called K-MEM in order to... 

Centroid-based methods including k-means and fuzzy c-means are known as effective and easy-to-implement approaches to clustering purposes in many applications. However, these algorithms cannot be directly applied to supervised tasks. This paper thus presents a generative model extending the centroid-based clustering approach to be applicable to classification and regression tasks. Given an arbitrary loss function, the proposed approach, termed Supervised Fuzzy Partitioning (SFP), incorporates labels information into its objective function through a surrogate term penalizing the empirical risk. Entropy-based regularization is also employed to fuzzify the partition and to weight features,... 

The k-means is the most popular clustering algorithm, but, as it needs too many distance computations, its speed is dramatically fall down against high-dimensional data. Although, there are some quite fast variants proposed in literature, but, there is still much room for improvement against high-dimensional large-scale datasets. What proposed here, k-means-g*, is based on a simple geometric concept. For four distinct points, if distance between all pairs except one pair are known, then, a lower bound can be determined for the unknown distance. Utilizing this technique in the assignment step of the k-means, many high-dimensional distance computations can be easily ignored, where small amount... 

We study the problem of preclustering a set B of imprecise points in Rd: we wish to cluster the regions specifying the potential locations of the points such that, no matter where the points are located within their regions, the resulting clustering approximates the optimal clustering for those locations. We consider k-center, k-median, and k-means clustering, and obtain the following results. Let B: = { b1, … , bn} be a collection of disjoint balls in Rd, where each ball bi specifies the possible locations of an input point pi. A partition C of B into subsets is called an (f(k) , α) -preclustering (with respect to the specific k-clustering variant under consideration) if (i) C consists of... 

We study the problem of preclustering a set B of imprecise points in Rd: we wish to cluster the regions specifying the potential locations of the points such that, no matter where the points are located within their regions, the resulting clustering approximates the optimal clustering for those locations. We consider k-center, k-median, and k-means clustering, and obtain the following results. Let B := {b1, . . ., bn} be a collection of disjoint balls in Rd, where each ball bi specifies the possible locations of an input point pi. A partition C of B into subsets is called an (f(k), α)preclustering (with respect to the specific k-clustering variant under consideration) if (i) C consists of... 

COVID-19 pandemic caused an increasing demand for online academic classes, which led to the demand for effective online exams with regards to limitations on time and resources. Consequently, holding online exams with sufficient reliability and effectiveness became one of the most critical and challenging subjects in higher education. Therefore, it is essential to have a preventive algorithm to allocate time and financial resources effectively. In the present study, a fair test with sufficient validity is first defined, and then by analogy with an engineering product, the design process is implemented on it. For this purpose, a hybrid method based on FMEA, which is a preventive method to... 

Clustering is the process of grouping data objects into set of disjoint classes called clusters so that objects within a class are highly similar with one another and dissimilar with the objects in other classes. The k-means algorithm is a simple and efficient algorithm that is widely used for data clustering. However, its performance depends on the initial state of centroids and may trap in local optima. In order to overcome local optima obstacles, a lot of studies have been done in clustering. The Fashion Algorithm is one effective method for searching problem space to find a near optimal solution. This paper presents a hybrid optimization algorithm based on Generalized Fashion Algorithm... 

Disease association study is of great importance among various fields of study in bioinformatics. Computational methods happen to be advantageous specifically when experimental approaches fail to obtain accurate results. Haplotypes are believed to be the most responsible biological data for genetic diseases. In this paper, the problem of reconstructing haplotypes from error-containing SNP fragments is discussed For this purpose, two new methods have been proposed by a combination of k-means clustering and particle swarm optimization algorithm. The methods and their implementation results on real biological and simulation datasets are represented which shows that they outperform the methods... 

A nonlinear integer programming model is developed in this article to solve redundancy allocation problems with multiple components having different failure rates in the series–parallel configuration using an active strategy. The main objective of this research is to select the number and the type of each component in subsystems so as the reliability of the system under certain constraints is maximized. To this aim, a weighted K-means clustering method is proposed, in which the analytical network process is employed to assign weights to the components of each cluster. As the proposed model belongs to the class of nondeterministic polynomial-time hardness problems, precise solution methods... 

The problem of allocating heterogeneous fleet of vehicles to the existing distribution network for dispensing products fro. manufacturing firm t. set of depots is considered. It is assume. heterogeneous fleet of vehicles with the given capacities and total costs consisting o. discounted fixed cost an. variable cost proportional to the amount shipped is employed for handling products. To minimize the total transportation costs, the problem is modeled i. form of the nonlinear mixed integer program. Due to the NP hard complexity of the mathematical model, three prioritized K-mean clustering hybrid GAs, by incorporating two new heuristic algorithms, are proposed. The efficiency of the algorithms... 

In this paper we introduce a preprocessing method to reduce noise effects in noise prone environments. Prototype based clustering algorithms are sensitive to noise because the effect of noisy data are as same as effect of true data and this affects on calculation of clusters center and then reduces accuracy. Therefore, these algorithms can not be applied in noise-prone environments and if this is applied there, we can not trust to the results. To overcome such problems we reduce and in some cases eliminate the noisy data. Also a part of our method is applied on the source of generated data in a network. Then noisy data that the number of them is high in noisy environments are eliminated and... 

In this paper, a fast and accurate algorithm for identifying circular objects in images is proposed. The presented method is a robust, fast and optimized adaption of Circular Hough Transform (CHT), Eigenvalues of Covariance Matrix and K-means clustering techniques. Results are greatly improved by implementing iterative K-means clustering algorithm and establishing an exponential growth instead of updating values in the parameter space of CHT through summation, both in runtime and quality. In fact, using the Eigenvalues of Covariance Matrix as a validating method, a well balanced compromise between the speed and accuracy of results is achieved. This method is tested on several real world... 

Clustering is currently one of the most crucial techniques for dealing with massive amount of heterogeneous information on the web, which is beyond human being's capacity to digest. Recent studies have shown that the most commonly used partitioning-based clustering algorithm, the K-means algorithm, is more suitable for large datasets. However, the K-means algorithm can generate a local optimal solution. In this paper we present novel harmony search clustering algorithms that deal with documents clustering based on harmony search optimization method. By modeling clustering as an optimization problem, first, we propose a pure harmony search based clustering algorithm that finds near global... 

Mutations in Single Nucleotide Polymorphisms (SNPs - different variant positions (1%) from human genomes) are responsible for some genetic diseases. As a consequence, obtaining all SNPs from human populations is one of the primary goals of recent studies in human genomics. Two sequences of mentioned SNPs in diploid human organisms are called haplotypes. In this paper, we study haplotype reconstruction from SNP-fragments with and without genotype information, problems. Designing serial and parallel classifiers was center of our research. Genetic algorithm and K-means were two components of our approaches. This combination helps us to cover the single classifier's weaknesses. ©2008 IEEE