Turcan G.Peker S.2024-03-092024-03-09202397898933479282166-0727https://doi.org/10.23919/CISTI58278.2023.10211598https://hdl.handle.net/20.500.14034/154118th Iberian Conference on Information Systems and Technologies, CISTI 2023 -- 20 June 2023 through 23 June 2023 -- -- 191760Alzheimer's disease has long been present and has affected a significant number of people throughout its history, resulting in a vast amount of data for researchers. Making sense of that amount of data depends on the appropriate data analysis methods and algorithms. The purpose of this study is to profile individuals with dementia by conducting a cluster analysis. A two-stage clustering analysis is employed to group individuals with dementia or without dementia regarding the key features of Alzheimer's disease. Two-stage clustering analysis firstly enables one to determine the groups of individuals they belong to, and after that, it provides a way to profile individuals with Alzheimer's disease according to the features in the Alzheimer Features dataset. Although a single data exploration analysis is used in this study, its contribution to the literature is quite high because the literature lacks clustering studies conducted on the same dataset features. In further research, it is recommended to use clustering-based classification methods for the same or similar datasets. © 2023 ITMA.eninfo:eu-repo/semantics/closedAccessalzheimer's disease; cluster analysis; data mining; dementia; two-stage clusteringClassification (of information); Data mining; Neurodegenerative diseases; Alzheimer; Alzheimers disease; Clustering analysis; Clusterings; Data analysis algorithms; Data analysis-methods; Dementia; Key feature; Number of peoples; Two-stage clustering; Cluster analysisConference Object10.23919/CISTI58278.2023.102115982023-June2-s2.0-85169805856N/A