Clustering Analysis with Embedding Vectors: An Application to Real Estate Market Delineation

Abstract

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, are applied to the land sales records to delineate the real estate market in Gwacheon-si, Gyeonggi province, South Korea. Then, the relevance of the resultant submarkets is evaluated using the root mean squared errors (RMSE) obtained from a hedonic pricing model. The results show that the RMSE in the embedding vector-based algorithm decreases substantially from 0.076-0.077 to 0.069. This study shows that the clustering algorithm empowered by embedding vectors outperforms the conventional algorithms, thereby enhancing the relevance of the delineated submarkets.

Comprehensive Safety Index for Road Safety Management System

“…Using the indicators at the administrative-district level, we performed a cluster analysis to determine the implications of using the developed indicators for regional safety level comparison. K-means clustering was utilized for cluster analysis [33,34]. K-means clustering [35] is an analysis technique that groups n data into k clusters.…”

“…Regarding K-means cluster analysis and hierarchical cluster analysis, density-based clustering employs a methodology that clusters regions with dense concentrations of Using the indicators at the administrative-district level, we performed a cluster analysis to determine the implications of using the developed indicators for regional safety level comparison. K-means clustering was utilized for cluster analysis [33,34]. K-means clustering [35] is an analysis technique that groups n data into k clusters.…”