High Dimensional Data Clustering using Self-Organized Map

Ruth Ema Febrita (Brawijaya University)
Wayan Firdaus Mahmudy (Brawijaya University)
Aji Prasetya Wibawa (Malang State University)

Knowledge Engineering and Data Science
Vol 2, No 1 (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.