A MATHEMATICAL MODEL FOR UNIFORM DISTRIBUTION OF VOTERS PER CONSTITUENCIES

This paper presents two different approaches on the basis how to generate constituencies. The first one is based on cluster analysis by means of which approach can get compact constituencies having an approximately equal number of voters. An optimal number of constituencies can be obtained by using this method. The second approach is based on partitioning the country into several areas with respect to territorial integrity of bigger administrative units. The units obtained in this way will represent constituencies which do not necessarily have to have an approximately equal number of voters. Each constituency is associated with a number of representatives that is proportional to its number of voters, so the problem is reduced to the integer approximation problem. Finally, these two approaches are combined and applied on the Republic of Croatia

Optimization of Clustering Algorithm Using Metaheuristic

A vital issue in information grouping and present a few answers for it. We explore utilizing separation measures other than Euclidean sort for enhancing the execution of Clustering. We additionally build up another point symmetry-based separation measure and demonstrate its proficiency. We build up a novel successful k-Mean calculation which enhances the execution of the k-mean calculation. We build up a dynamic linkage grouping calculation utilizing kd-tree and we demonstrate its superior. The Automatic Clustering Differential Evolution (ACDE) is particular to Clustering basic information sets and finding the ideal number of groups consequently. We enhance ACDE for arranging more mind boggling information sets utilizing kd-tree. The proposed calculations don't have a most pessimistic scenario bound on running time that exists in numerous comparable calculations in the writing. Experimental results appeared in this proposition exhibit the viability of the proposed calculations. We contrast the proposed calculations and other ACO calculations. We display the proposed calculations and their execution results in point of interest alongside promising streets of future examination

Data clustering for circle detection

This paper considers a multiple-circle detection problem on the basis of given data. The problem is solved by application of the center-based clustering method. For the purpose of searching for a locally optimal partition modeled on the well-known k-means algorithm, the k-closest circles algorithm has been constructed. The method has been illustrated by several numerical examples

Data clustering for circle detection

This paper considers a multiple-circle detection problem on the basis of given data. The problem is solved by application of the center-based clustering method. For the purpose of searching for a locally optimal partition modeled on the well-known k-means algorithm, the k-closest circles algorithm has been constructed. The method has been illustrated by several numerical examples

Cluster analysis and artificial neural networks in predicting energy efficiency of public buildings as a cost-saving approach

Although energy efficiency is a hot topic in the context of global climate change, in the European Union directives and in national energy policies, methodology for estimating energy efficiency still relies on standard techniques defined by experts in the field. Recent research shows a potential of machine learning methods that can produce models to assess energy efficiency based on available previous data. In this paper, we analyse a real dataset of public buildings in Croatia, extract their most important features based on the correlation analysis and chi-square tests, cluster the buildings based on three selected features, and create a prediction model of energy efficiency for each cluster of buildings using the artificial neural network (ANN) methodology. The main objective of this research was to investigate whether a clustering procedure improves the accuracy of a neural network prediction model or not. For that purpose, the symmetric mean average percentage error (SMAPE) was used to compare the accuracy of the initial prediction model obtained on the whole dataset and the separate models obtained on each cluster. The results show that the clustering procedure has not increased the prediction accuracy of the models. Those preliminary findings can be used to set goals for future research, which can be focused on estimating clusters using more features, conducted more extensive variable reduction, and testing more machine learning algorithms to obtain more accurate models which will enable reducing costs in the public sector

Color image segmentation based on intensity and hue clustering - a comparison of LS and LAD approaches

Motivated by the method for color image segmentation based on intensity and hue clustering proposed in [26] we give some theoretical explanations for this method that directly follows from the natural connection between the maximum likelihood approach and Least Squares or Least Absolute Deviations clustering optimality criteria. The method is tested and illustrated on a few typical situations, such as the presence of outliers among the data

A MATHEMATICAL MODEL FOR UNIFORM DISTRIBUTION OF VOTERS PER CONSTITUENCIES

This paper presents two different approaches on the basis how to generate constituencies. The first one is based on cluster analysis by means of which approach can get compact constituencies having an approximately equal number of voters. An optimal number of constituencies can be obtained by using this method. The second approach is based on partitioning the country into several areas with respect to territorial integrity of bigger administrative units. The units obtained in this way will represent constituencies which do not necessarily have to have an approximately equal number of voters. Each constituency is associated with a number of representatives that is proportional to its number of voters, so the problem is reduced to the integer approximation problem. Finally, these two approaches are combined and applied on the Republic of Croatia

Clustering Algorithms For High Dimensional Data – A Survey Of Issues And Existing Approaches

Clustering is the most prominent data mining technique used for grouping the data into clusters based on distance measures. With the advent growth of high dimensional data such as microarray gene expression data, and grouping high dimensional data into clusters will encounter the similarity between the objects in the full dimensional space is often invalid because it contains different types of data. The process of grouping into high dimensional data into clusters is not accurate and perhaps not up to the level of expectation when the dimension of the dataset is high. It is now focusing tremendous attention towards research and development. The performance issues of the data clustering in high dimensional data it is necessary to study issues like dimensionality reduction, redundancy elimination, subspace clustering, co-clustering and data labeling for clusters are to analyzed and improved. In this paper, we presented a brief comparison of the existing algorithms that were mainly focusing at clustering on high dimensional data

A simulated annealing-based maximum-margin clustering algorithm

© 2018 Wiley Periodicals, Inc. Maximum-margin clustering is an extension of the support vector machine (SVM) to clustering. It partitions a set of unlabeled data into multiple groups by finding hyperplanes with the largest margins. Although existing algorithms have shown promising results, there is no guarantee of convergence of these algorithms to global solutions due to the nonconvexity of the optimization problem. In this paper, we propose a simulated annealing-based algorithm that is able to mitigate the issue of local minima in the maximum-margin clustering problem. The novelty of our algorithm is twofold, ie, (i) it comprises a comprehensive cluster modification scheme based on simulated annealing, and (ii) it introduces a new approach based on the combination of k-means++ and SVM at each step of the annealing process. More precisely, k-means++ is initially applied to extract subsets of the data points. Then, an unsupervised SVM is applied to improve the clustering results. Experimental results on various benchmark data sets (of up to over a million points) give evidence that the proposed algorithm is more effective at solving the clustering problem than a number of popular clustering algorithms