Data clustering for circle detection

Marošević, Tomislav

doi:10.17535/crorr.2014.0025

Cited by 5 publications

(4 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Metode raspodjele ukupnog broja zastupničkih mjesta S po izbornim jedinicama I 1 , …, I k mogu se općenito podijeliti u dvije skupine (v. Balinski & Joung (1975); Cortona et al (1999); Gallagher (1992); Marošević, Sabo & Taler (2013)):…”

Section: Metode Raspodjele Broja Zastupničkih Mjestaunclassified

“…U nastavku navodimo poznate indekse 3 kojima je moguće mjeriti kvalitetu raspodjele broja zastupničkih mjesta po izbornim jedinicama koje na neki način mjere ujednačenost težine biračkog glasa (v. Marošević, Sabo & Taler, 2013). Pritom će manja vrijednost indeksa značiti ujednačenije težine biračkih glasova.…”

Section: Mjere Ujednačenosti Težina Biračkih Glasovaunclassified

See 1 more Smart Citation

Nova metoda za definiranje izbornih jedinica u Hrvatskoj

Sabo,

Scitovski

2024

HKJU-CCPA

View full text Add to dashboard Cite

U radu predlažemo novu metodu za definiranje konfiguracije izbornih jedinica primjenom metode spektralnog klasteriranja. Metoda pronalazi konfiguracije izbornih jedinica koje zadovoljavaju neku unaprijed zadanu toleranciju ujednačenosti težina biračkih glasova te pritom čuva granice županija. Također, u metodu se prirodno može uključiti i razina političke/socijalne/gospodarske povezanosti županija. Nadalje, navodimo popis poznatih metoda za određivanje broja zastupničkih mjesta po izbornim jedinicama, koje se temelje na načelu razmjernosti broja birača i broja zastupničkih mjesta. U radu dajemo pregled indeksa iz literature kojima se može mjeriti ujednačenost težina biračkih glasova. Primjene tih indeksa ilustriramo na najnovijem prijedlogu Hrvatske vlade, vlastitim prijedlozima te na nekoliko primjera konfiguracija izbornih jedinica od kojih su neki već predstavljeni javnosti.

show abstract

Section: Metode Raspodjele Broja Zastupničkih Mjestaunclassified

Section: Mjere Ujednačenosti Težina Biračkih Glasovaunclassified

Nova metoda za definiranje izbornih jedinica u Hrvatskoj

Sabo,

Scitovski

2024

HKJU-CCPA

View full text Add to dashboard Cite

show abstract

“…Clustering is a widely used exploratory data analysis tool that has been successfully applied to data analysis, image processing, pattern recognition, engineering [2,4,6,7,8,15,17,18], and many other fields. In this paper, we focus on the detection of clusters in a noisy environment based on the well-known EM algorithm [2,3,9,11,18].…”

Section: Introductionmentioning

confidence: 99%

Cluster Detection in the Noisy Environment by Using the Modified EM Algorithm

Novoselac

Pavić

2018

Cro.Oper.Res.Rev.

View full text Add to dashboard Cite

The paper studies the problem of cluster detection in noisy environment. The solution of this problem is based on the well known Expectation Maximization (EM) algorithm. By utilizing the Mahalanobis distance, and modifying the hidden variable, the rejection procedure is constructed so that it omits data from calculation of the current iteration step. Thus we construct the adaptive framework for solving the above problem. Several numerical examples are presented to illustrate the proposed algorithm.

show abstract

“…where D(a, E) is the distance from point a to ellipse E (an analogous definition for the circle-center is given in [13,18]). If the Mahalanobis distance-like function is used (see, e.g., [2,3,21]…”

Section: Introductionmentioning

confidence: 99%

Multiple ellipse fitting by center-based clustering

Marošević

Scitovski

2015

Croat. Oper. Res. Rev.

Self Cite

View full text Add to dashboard Cite

Abstract. This paper deals with the multiple ellipse fitting problem based on a given set of data points in a plane. The presumption is that all data points are derived from k ellipses that should be fitted. The problem is solved by means of center-based clustering, where cluster centers are ellipses. If the Mahalanobis distance-like function is introduced in each cluster, then the cluster center is represented by the corresponding Mahalanobis circle-center. The distance from a point a ∈ R 2 to the Mahalanobis circle is based on the algebraic criterion. The well-known k-means algorithm has been adapted to search for a locally optimal partition of the Mahalanobis circle-centers. Several numerical examples are used to illustrate the proposed algorithm.

show abstract

Data clustering for circle detection

Cited by 5 publications

References 19 publications

Nova metoda za definiranje izbornih jedinica u Hrvatskoj

Nova metoda za definiranje izbornih jedinica u Hrvatskoj

Cluster Detection in the Noisy Environment by Using the Modified EM Algorithm

Multiple ellipse fitting by center-based clustering

Contact Info

Product

Resources

About