prikaz prve stranice dokumenta Comparison of Clustering Algorithms for Optimal Restaurant Location Selection Using Location-Based Social Networks Data
  Download
PDF 9.49 MB
Scientific paper - Original scientific paper
Comparison of Clustering Algorithms for Optimal Restaurant Location Selection Using Location-Based Social Networks Data
In: Međunarodni znanstveni simpozij Gospodarstvo istočne Hrvatske – jučer, danas, sutra. Osijek: Ekonomski fakultet Sveučilišta Josipa Jurja Strossmayera u Osijeku, 2021. pp. 677-690. urn:nbn:hr:112:377078
Đokić, Kristian; Potnik Galić, Katarina; Štavlić, Katarina

Cite this document

Đokić, K., Potnik Galić, K. & Štavlić, K. (2021). Comparison of Clustering Algorithms for Optimal Restaurant Location Selection Using Location-Based Social Networks Data. Međunarodni znanstveni simpozij Gospodarstvo istočne Hrvatske – jučer, danas, sutra (pp. 677-690). Osijek: Ekonomski fakultet Sveučilišta Josipa Jurja Strossmayera u Osijeku. Retrieved from https://urn.nsk.hr/urn:nbn:hr:112:377078

Đokić, Kristian, et al. "Comparison of Clustering Algorithms for Optimal Restaurant Location Selection Using Location-Based Social Networks Data." Međunarodni znanstveni simpozij Gospodarstvo istočne Hrvatske – jučer, danas, sutra, Ekonomski fakultet Sveučilišta Josipa Jurja Strossmayera u Osijeku, 2021, pp. 677-690. https://urn.nsk.hr/urn:nbn:hr:112:377078

Đokić, Kristian, Katarina Potnik Galić and Katarina Štavlić. "Comparison of Clustering Algorithms for Optimal Restaurant Location Selection Using Location-Based Social Networks Data." In Međunarodni znanstveni simpozij Gospodarstvo istočne Hrvatske – jučer, danas, sutra, 677-690. Osijek: Ekonomski fakultet Sveučilišta Josipa Jurja Strossmayera u Osijeku, 2021. Accessed 2025 January 21. https://urn.nsk.hr/urn:nbn:hr:112:377078

Đokić, K., Potnik Galić, K. and Štavlić, K. (2021) 'Comparison of Clustering Algorithms for Optimal Restaurant Location Selection Using Location-Based Social Networks Data', Međunarodni znanstveni simpozij Gospodarstvo istočne Hrvatske – jučer, danas, sutra, Ekonomski fakultet Sveučilišta Josipa Jurja Strossmayera u Osijeku, Osijek, pp. 677-690. Available at: https://urn.nsk.hr/urn:nbn:hr:112:377078 (Accessed 21 January 2025)

Đokić K, Potnik Galić K, Štavlić K. Comparison of Clustering Algorithms for Optimal Restaurant Location Selection Using Location-Based Social Networks Data. Međunarodni znanstveni simpozij Gospodarstvo istočne Hrvatske – jučer, danas, sutra. Osijek: Ekonomski fakultet Sveučilišta Josipa Jurja Strossmayera u Osijeku; 2021. Pp. 677-690. [cited 2025 January 21] Available at: https://urn.nsk.hr/urn:nbn:hr:112:377078

K. Đokić, K. Potnik Galić and K. Štavlić, "Comparison of Clustering Algorithms for Optimal Restaurant Location Selection Using Location-Based Social Networks Data", Međunarodni znanstveni simpozij Gospodarstvo istočne Hrvatske – jučer, danas, sutra Osijek: Ekonomski fakultet Sveučilišta Josipa Jurja Strossmayera u Osijeku, 2021. [Online] Available at: https://urn.nsk.hr/urn:nbn:hr:112:377078 [Accessed: 21 January 2025]

Please login to the repository to save this object to your list.
Metadata
Title (english)Comparison of Clustering Algorithms for Optimal Restaurant Location Selection Using Location-Based Social Networks Data
AuthorKristian Đokić
AuthorKatarina Potnik Galić
AuthorKatarina Štavlić
Author's institutionPolytehnic in Pozega
Scientific / art field,
discipline and subdiscipline		SOCIAL SCIENCES
Economics
Scientific / art field,
discipline and subdiscipline		SOCIAL SCIENCES
Information and Communication Sciences
Abstract (english)
Machine learning algorithms are increasingly used in various fields. Unlike supervised algorithms that require the engagement and knowledge of experts in a particular area, unsupervised algorithms do not need it and are therefore more comfortable to use. Clustering algorithms belong to unsupervised algorithms and are used to group data according to a given similarity criterion with achieving significant similarity between data within the same group and minor similarities between data belonging to different groups. In this paper, five clustering algorithms in restaurant location optimization in Zagreb are analyzed. The clustering algorithms' output result lists municipalities in Zagreb city divided into groups with similar properties. Based on these data, the investor can quickly conclude what individual municipalities are similar and based on that, a more objective assessment of the location of a restaurant or catering facility can be made before the investment. The data based on which the algorithms divided parts of Zagreb into groups were obtained from a social network that can store user locations. One of the essential functions of the used social network is sharing information about restaurants, cafes, and other catering facilities. The common name of these social networks is a location-based social network. The paper compares the Gaussian Mixture Model algorithm, k- means algorithm, Hierarchies algorithm, Agglomerative Clustering algorithm, and Spectral Clustering algorithm. The selected five algorithms have the property that one of their input variables is the number of clusters.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review - international
Publication versionPublished version
Publication titleMeđunarodni znanstveni simpozij Gospodarstvo istočne Hrvatske – jučer, danas, sutra
Numberingpp. 677-690
p-ISSN1848-9559
URN:NBNurn:nbn:hr:112:377078
Publication2021
ConferenceTitle: 10th International Scientific Symposium Region, Entrepreneurship, Development (RED 2021)
Location: Osijek, Croatia
Start date: 2021-06-17
End date: 2021-06-17
Assembly type: Lecture
Type of resourceText
PublisherEkonomski fakultet Sveučilišta Josipa Jurja Strossmayera u Osijeku
Publishing placeOsijek
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2024-05-06 10:04:31