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.
First Page
31
Last Page
40
Recommended Citation
Febrita, R. E.,
Mahmudy, W. F.,
&
Wibawa, A. P.
(2019).
High Dimensional Data Clustering using Self-Organized Map.
Knowledge Engineering and Data Science, 2(1), 31-40.
DOI: https://doi.org/10.17977/um018v2i12019p31-40
Available at:
https://citeus.um.ac.id/keds/vol2/iss1/9