Year: 2023
Type: conference-paper
Abstract: The health and medicine sector is currently experiencing significant transformations, such as the integration of artificial intelligence in the decision-making process. In this complex system, there is a continuous data flow consisting of quantitative, qualitative, ordinal types, and time series. Hierarchical clustering is a useful tool to handle this complexity. However, clustering mixed data containing time series without distorting the inherent nature of the data poses a challenge. Although there are existing clustering techniques for mixed data or time series, the literature does not address the clustering of mixed data and time series. This paper presents several methodologies for such data clustering, including a novel algorithm based on pretopology. This hierarchical algorithm allows for customizable logical clustering, enabling health experts to better interpret and utilize the results for classification and recommendation by analyzing the hierarchy of clusters.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4389366116