High Dimensional Data Clustering using Self-Organized Map

Authors

Ruth Ema Febrita, Universitas Brawijaya, IndonesiaFollow
Wayan Firdaus Mahmudy, Universitas Brawijaya, IndonesiaFollow
Aji Prasetya Wibawa, State University of Malang, IndonesiaFollow

Abstract

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.

DOI

10.17977/um018v2i12019p31-40

Recommended Citation

Febrita, Ruth Ema; Mahmudy, Wayan Firdaus; and Wibawa, Aji Prasetya (2019) "High Dimensional Data Clustering using Self-Organized Map," Knowledge Engineering and Data Science: Vol. 2: No. 1, Article 9.
DOI: 10.17977/um018v2i12019p31-40
Available at: https://citeus.um.ac.id/keds/vol2/iss1/9