High Dimensional Data Clustering using Self-Organized Map
| Autor: | Ruth Ema Febrita, Wayan Firdaus Mahmudy, Aji Prasetya Wibawa |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | Knowledge Engineering and Data Science, Vol 2, Iss 1, Pp 31-40 (2019) |
| Druh dokumentu: | article |
| ISSN: | 2597-4602 2597-4637 |
| DOI: | 10.17977/um018v2i12019p31-40 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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