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Journal : Knowledge Engineering and Data Science

High Dimensional Data Clustering using Self-Organized Map Ruth Ema Febrita; Wayan Firdaus Mahmudy; Aji Prasetya Wibawa
Knowledge Engineering and Data Science Vol 2, No 1 (2019)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1028.763 KB) | DOI: 10.17977/um018v2i12019p31-40

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.