Data on cut-edge for spatial clustering based on proximity graphs
| dc.authorid | 0000-0001-6657-9738 | |
| dc.contributor.author | Aksaç, Alper | |
| dc.contributor.author | Özyer, Tansel | |
| dc.contributor.author | Alhajj, Reda | |
| dc.date.accessioned | 2020-04-15T11:14:01Z | |
| dc.date.available | 2020-04-15T11:14:01Z | |
| dc.date.issued | 2020 | |
| dc.department | İstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü | |
| dc.description.abstract | Cluster analysis plays a significant role regarding automating such a knowledge discovery process in spatial data mining. A good clustering algorithm supports two essential conditions, namely high intra-cluster similarity and low inter-cluster similarity. Maximized intra-cluster/within-cluster similarity produces low distances between data points inside the same cluster. However, minimized inter-cluster/between-cluster similarity increases the distance between data points in different clusters by furthering them apart from each other. We previously presented a spatial clustering algorithm, abbreviated CutESC (Cut-Edge for Spatial Clustering) with a graph-based approach. The data presented in this article is related to and supportive to the research paper entitled "CutESC: Cutting edge spatial clustering technique based on proximity graphs" (Aksac et al., 2019) [1], where interpretation research data presented here is available. In this article, we share the parametric version of our algorithm named CutESC-P, the best parameter settings for the experiments, the additional analyses and some additional information related to the proposed algorithm (CutESC) in [1]. | |
| dc.identifier.citation | Aksaç, A., Özyer, T. ve Alhajj, R. (2020). Data on cut-edge for spatial clustering based on proximity graphs. Data in Brief, 28. https://dx.doi.org/10.1016/j.dib.2019.104899 | |
| dc.identifier.doi | 10.1016/j.dib.2019.104899 | |
| dc.identifier.issn | 2352-3409 | |
| dc.identifier.scopusquality | Q4 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12511/5122 | |
| dc.identifier.uri | https://dx.doi.org/10.1016/j.dib.2019.104899 | |
| dc.identifier.volume | 28 | |
| dc.identifier.wosquality | N/A | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.indekslendigikaynak | PubMed | |
| dc.language.iso | en | |
| dc.publisher | Elsevier | |
| dc.relation.ispartof | Data in Brief | en_US |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | Attribution 4.0 International | * |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | * |
| dc.subject | Spatial Data Mining | |
| dc.subject | Clustering | |
| dc.subject | Proximity Graphs | |
| dc.subject | Graph Theory | |
| dc.title | Data on cut-edge for spatial clustering based on proximity graphs | |
| dc.type | Article |
