Data on cut-edge for spatial clustering based on proximity graphs
Göster/ Aç
Erişim
info:eu-repo/semantics/openAccessAttribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/Tarih
2020Üst veri
Tüm öğe kaydını gösterKünye
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.2019.104899Özet
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].
Scopus Q Kategorisi
Q4Kaynak
Data in BriefCilt
28Koleksiyonlar
Aşağıdaki lisans dosyası bu öğe ile ilişkilidir: