# Croatian Operational Research Review,Vol. 1 No. 1, 2010

Original scientific paper

DATA CLUSTERING: APPLICATIONS IN ENGINEERING

Zdravko Krpić ; Faculty of Electrical Engineering, University of Osijek, Osijek, Croatia
Goran Martinović   orcid.org/0000-0002-7469-6018 ; Faculty of Electrical Engineering, University of Osijek, Osijek, Croatia
Ivan Vazler ; Department of Mathematics, University of Osijek, Osijek, Croatia

 Fulltext: english, pdf (520 KB) pages 180-189 downloads: 306* cite APA 6th EditionKrpić, Z., Martinović, G. & Vazler, I. (2010). DATA CLUSTERING: APPLICATIONS IN ENGINEERING. Croatian Operational Research Review, 1 (1), 180-189. Retrieved from https://hrcak.srce.hr/94967 MLA 8th EditionKrpić, Zdravko, et al. "DATA CLUSTERING: APPLICATIONS IN ENGINEERING." Croatian Operational Research Review, vol. 1, no. 1, 2010, pp. 180-189. https://hrcak.srce.hr/94967. Accessed 22 Oct. 2021. Chicago 17th EditionKrpić, Zdravko, Goran Martinović and Ivan Vazler. "DATA CLUSTERING: APPLICATIONS IN ENGINEERING." Croatian Operational Research Review 1, no. 1 (2010): 180-189. https://hrcak.srce.hr/94967 HarvardKrpić, Z., Martinović, G., and Vazler, I. (2010). 'DATA CLUSTERING: APPLICATIONS IN ENGINEERING', Croatian Operational Research Review, 1(1), pp. 180-189. Available at: https://hrcak.srce.hr/94967 (Accessed 22 October 2021) VancouverKrpić Z, Martinović G, Vazler I. DATA CLUSTERING: APPLICATIONS IN ENGINEERING. Croatian Operational Research Review [Internet]. 2010 [cited 2021 October 22];1(1):180-189. Available from: https://hrcak.srce.hr/94967 IEEEZ. Krpić, G. Martinović and I. Vazler, "DATA CLUSTERING: APPLICATIONS IN ENGINEERING", Croatian Operational Research Review, vol.1, no. 1, pp. 180-189, 2010. [Online]. Available: https://hrcak.srce.hr/94967. [Accessed: 22 October 2021]

Abstracts
Dividing a set S $\mathcal{S} = \{x_i=(x_1^{(i)}+\dots+x_n^{(i)})^T \in \mathbb{R}^n:i=1,\dots,m\}$ (a set of vectors from a vector space $\mathbb{R}^n$) into disjunct subsets $\pi_1,\dots,\pi_k, 1\leq k\leq m$, such that
$\cup_{i=1}^k \pi_i=S, \pi_i \cap \pi_j=0, i \ne j, |\pi_j|\geq 1, j=1,\dots,k$,
determines a partition of the set $\mathcal{S}$. The elements of such partition $\pi_1,\dots,\pi_k$ are called clusters.
For practical clustering applications the number of all clusters is too big and the problem of determining the optimal partition in the least-squares sense is an NP-hard problem.
In this paper we will consider some well-known algorithms for searching for an optimal LS-partition, list some of the numerous applications of cluster analysis in engineering and give some practical applications.

Keywords
data clustering; engineering; least squares

Hrčak ID: 94967

Visits: 529 *