High Dimensional Data Clustering using Self-Organized Map

Ruth Ema Febrita, Wayan Firdaus Mahmudy, Aji Prasetya Wibawa


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.

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DOI: http://dx.doi.org/10.17977/um018v2i12019p31-40


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