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Clustering Analysis with Embedding Vectors: An Application to Real Estate Market Delineation
2021
Advances in Technology Innovation
Although clustering analysis is a popular tool in unsupervised learning, it is inefficient for the datasets dominated by categorical variables, e.g., real estate datasets. To apply clustering analysis to real estate datasets, this study proposes an entity embedding approach that transforms categorical variables into vector representations. Three variants of a clustering algorithm, i.e., the clustering based on the traditional Euclidean distance, the Gower distance, and the embedding vectors,
doi:10.46604/aiti.2021.8492
fatcat:isk4oah2hbe4pecqnoxk77encq