Professor retired

Šime Ungar

sime@mathos.hr
School of Applied Mathematics and Informatics

Josip Juraj Strossmayer University of Osijek

Research Interests

  • Geometric and Algebraic Topology
  • Shape theory

Degrees

  • PhD in mathematics, Department of Mathematics, University of Zagreb , 1977.
  • MSc in mathematics, Department of Mathematics, University of Zagreb , 1972.
  • BSc in mathematics, Department of Mathematics, University of Zagreb, 1969.

Publications

Journal Publications

  1. K. Sabo, R. Scitovski, Š. Ungar, Z. Tomljanović, A method for searching for a globally optimal k-partition of higher-dimensional datasets, Journal of Global Optimization 89 (2024), 633-653
    The problem with finding a globally optimal k-partition of a set A is a very intricate optimization problem for which in general, except in the case of one-dimensional data, i.e., for data with one feature (A\subset\R), there is no method to solve. Only in the one-dimensional case there exist efficient methods that are based on the fact that the search for a globally optimal partition is equivalent to solving a global optimization problem for a symmetric Lipschitz-continuous function using the global optimization algorithm DIRECT. In the present paper, we propose a method for finding a globally optimal k-partition in the general case (A\subset \R^n, n\geq 1), generalizing an idea for solving the Lipschitz global optimization for symmetric functions. To do this, we propose a method that combines a global optimization algorithm with linear constraints and the k-means algorithm. The first of these two algorithms is used only to find a good initial approximation for the $k$-means algorithm. The method was tested on a number of artificial datasets and on several examples from the UCI Machine Learning Repository, and an application in spectral clustering for linearly non-separable datasets is also demonstrated. Our proposed method proved to be very efficient.
  2. R. Scitovski, K. Sabo, D. Grahovac, F. Martínez-Álvarez, Š. Ungar, A partitioning incremental algorithm using adaptive Mahalanobis fuzzy clustering and identifying the most appropriate partition, Pattern Analysis and Applications 28/3 (2024), 1-14
    This paper deals with the problem of determining the most appropriate number of clusters in a fuzzy Mahalanobis partition. First, a new fuzzy Mahalanobis incremental algorithm is constructed to search for an optimal fuzzy Mahalanobis partition with clusters. Among these partitions, selecting the one with the most appropriate number of clusters is based on appropriately modified existing fuzzy indexes. In addition, the Fuzzy Mahalanobis Minimal Distance index is defined as a natural extension of the recently proposed Mahalanobis Minimal Distance index for non-fuzzy clustering. The new fuzzy Mahalanobis incremental algorithm was tested on several artificial data sets and the color image segmentation problems from real-world applications: art images, nature photography images, and medical images. The algorithm includes multiple usage of the global optimization algorithm DIRECT. But unlike previously known fuzzy Mahalanobis indexes, the proposed Fuzzy Mahalanobis Minimal Distance index ensures accurate results even when applied to complex real-world applications. A possible disadvantage could be the need for longer CPU time. Furthermore, besides effective identification of the partition with the most appropriate number of clusters, it is shown how to use the proposed Fuzzy Mahalanobis Minimal Distance index to search for an acceptable partition, which proved particularly useful in the above-mentioned real-world applications.
  3. A. Morales-Esteban, R. Scitovski, K. Sabo, D. Grahovac, Š. Ungar, Earthquake analysis of clusters of the most appropriate partition, Journal of Seismology (2024), prihvaćen za objavljivanje
    In our paper, we propose the most appropriate partition to depict the seismogenic zones of an active seismic region.To do so,the earthquake data considered are the location and magnitude. To determine three ellipsoidal layers of shallow, intermediate, and deep earthquakes, we switch from the geoid to a solid ball model and solve an appropriate multiple concentric sphere detection problem. Considering the Iberian Peninsula region, by using the Mahalanobis incremental algorithm with the help of the Mahalanobis area index and Mahalanobis minimal distance index, we first determine the most appropriate partition of earth quake positions, consisting of as compact and mutually separated clusters as possible. The result shows four clusters representing the main seismogenic zones of that area. In each of these clusters, we analyze some important earthquake properties, notably the hypocentral depths—a less researched property. Furthermore, we show how to generate a smooth surface best fitting the hypocenters in the considered area, and since the data contain many outliers, for that purpose we use the moving least absolute deviation method. In addition, for each cluster of the most appropriate partition, we ponder the question of estimating the Gutenberg–Richter’s b-value. To avoid the known drawbacks mentioned in the literature for estimating the b-parameter in the Gutenberg–Richter law, we propose the estimation of parameters a and b by using the least absolute deviation method. We also found that the hypocenters are notably deeper in the southwestern Iberian Peninsula and the Azores-Gibraltar fault zone, where the largest earthquakes take place. Finally, one should emphasizethat the hypocenters study proposed in this research demonstrated that the most hazardous zone encompasses the most deep focuses. The CPU-time required for all calculations has been moderate. The methodology, used in this work, could easily be applied to other seismological areas, for which we list our freely available Mathematica-modules.
  4. R. Scitovski, K. Sabo, D. Grahovac, Š. Ungar, Minimal distance index — A new clustering performance metrics, Information Sciences 640/119046 (2023)
    We define a new index for measuring clustering performance called the Minimal Distance Index. The index is based on representing clusters by characteristic objects containing the majority of cluster points. It performs well for both spherical and ellipsoidal clusters. This method can recognize all acceptable partitions with well-separated clusters. Among such partitions, our minimal distance index may identify the most appropriate one. The proposed index is compared with other most frequently used indexes in numerous examples with spherical and ellipsoidal clusters. It turned out that our proposed minimal distance index always recognizes the most appropriate partition, whereas the same cannot be said for other indexes found in the literature. Furthermore, among all acceptable partitions, the one with the largest number of clusters, not necessarily the most appropriate ones, has a special significance in image analysis. Namely, following Mahalanobis image segmentation, our index recognizes partitions that might not be the most appropriate ones but are the ones using colors that significantly differ from each other. The minimal distance index recognizes partitions with dominant colors, thus making it possible to select specific details in the image. We apply this approach to some real-world applications such as the plant rows detection problem, painting analysis, and iris detection. This may also be useful for medical image analysis.
  5. K. Sabo, R. Scitovski, Š. Ungar, Multiple spheres detection problem—Center based clustering approach, Pattern Recognition Letters 176 (2023), 34-41
    In this paper, we propose an adaptation of the well-known -means algorithm for solving the multiple spheres detection problem when data points are homogeneously scattered around several spheres. We call this adaptation the -closest spheres algorithm. In order to choose good initial spheres, we use a few iterations of the global optimizing algorithm DIRECT , resulting in the high efficiency of the proposed -closest spheres algorithm. We present illustrative examples for the case of non-intersecting and for the case of intersecting spheres. We also show a real-world application in analyzing earthquake depths.