High Dimensional Data Clustering using Self-Organized Map - Febrita, Ruth Ema; Mahmudy, Wayan Firdaus; Wibawa, Aji Prasetya (Knowledge Engineering and Data Science, 2019)
As the population grows and e economic development, houses could be one of basic needs of every family. Therefore, housing investment has promising value in the future. This research implements the Self-Organized Map (SOM) algorithm to cluster house data for providing several house groups based on the various features. K-means is used as the baseline of the proposed approach. SOM has higher silhouette coefficient (0.4367) compared to its comparison (0.236). Thus, this method outperforms k-means in terms of visualizing high-dimensional data cluster. It is also better in the cluster formation and regulating the data distribution.