Benchmarking distance-based partitioning methods for mixed-type data
Benchmarking distance-based partitioning methods for mixed-type data
Abstract Clustering mixed-type data, that is, observation by variable data that consist of both continuous and categorical variables poses novel challenges. Foremost among these challenges is the choice of the most appropriate clustering method for the data. This paper presents a benchmarking study comparing eight distance-based partitioning methods for mixed-type …