The k-means algorithm with the corresponding problem formulation is one of the first methods that researchers use when solving a new automatic grouping (clus-tering) problem. Its improvement, modification and combination with other algorithms are described in the works of many researchers. In this research, we propose new al-gorithms of the Greedy Heuristic Method, which use an idea of the search in variable neighborhoods for solving the classical cluster analysis problem, and allows us to obtain a more accurate and stable result of solving in comparison with the known algorithms. Our computational experiments show that the new algorithms allow us to obtain re-sults with better values of the objective function value (sum of squared distances) in comparison with classical algorithms such as k-means, j-means and genetic algorithms on various practically important datasets. In addition, we present the first results for the GPU realization of the Greedy Heuristic Method.

Progress in location theory methods and clustering algorithms is mainly targeted at improving the performance of the algorithms. The most popular clustering models are based on solving the p-median and similar location problems (k-means, k-medoids). In such problems, the algorithm must find several points called cluster centers, centroids, medoids, depending on the specific problem which minimize some function of distances from known objects to the centers. In the the k-medoids problem, the centers (medoids) of the cluster must coincide with one of the clustered objects. The problem is NP-hard, and the efforts of researchers are focused on the development of compromise heuristic algorithms that provide a fairly quick solution with minimal error. In this paper, we propose new algorithms of the Greedy Heuristic Method which use the idea of the Variable Neighborhood Search (VNS) algorithms for solving the k-medoids problem (which is also called the discrete p-median problem). In addition to the known PAM (Partition Around Medoids) algorithm, neighborhoods of a known solution are formed by applying greedy agglomerative heuristic procedures. According to the results of computational experiments, the new search algorithms (Greedy PAM-VNS) give more accurate and stable results (lower average value of the objective function and its standard deviation, smaller spread) in comparison with known algorithms on various data sets. Povzetek: Avtorji predlagajo nove algoritme za reševanje problema lokacije k-medoidov in gručenja.

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The k-means problem is one of the most popular models in cluster analysis that minimizes the sum of the squared distances from clustered objects to the sought cluster centers (centroids). The simplicity of its algorithmic implementation encourages researchers to apply it in a variety of engineering and scientific branches. Nevertheless, the problem is proven to be NP-hard which makes exact algorithms inapplicable for large scale problems, and the simplest and most popular algorithms result in very poor values of the squared distances sum. If a problem must be solved within a limited time with the maximum accuracy, which would be difficult to improve using known methods without increasing computational costs, the variable neighborhood search (VNS) algorithms, which search in randomized neighborhoods formed by the application of greedy agglomerative procedures, are competitive. In this article, we investigate the influence of the most important parameter of such neighborhoods on the computational efficiency and propose a new VNS-based algorithm (solver), implemented on the graphics processing unit (GPU), which adjusts this parameter. Benchmarking on data sets composed of up to millions of objects demonstrates the advantage of the new algorithm in comparison with known local search algorithms, within a fixed time, allowing for online computation.

The feature of the circular economy is the restorative and closed nature of the production cycle with the “green” nature-like technologies application which reduce greenhouse gas emissions, slow down the temperature rise on the planet and preserve the environment. The circular economy approaches correspond to the concept of goal-setting of the United Nations Organization in the mainstream of sustainable socio-economic development and are widely used in the countries of the European Union. As a part of their research, scientists have defined a circular economy as the economy which involves the total multiple processing of the resources applied and provides energy savings. In this regard, the circular economy is called “green”, i.e. preserving the natural resources of the planet and the environment on the basis of information technology. Currently, there is enough evidence that circularity has begun to permeate linear economics and that innovative products and contracts are already available in various forms.