High Dimensional Data Clustering using Self-Organized Map
| Autor: | Wayan Firdaus Mahmudy, Ruth Ema Febrita, Aji Prasetya Wibawa |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Clustering high-dimensional data
education.field_of_study Computer science Data cluster Population lcsh:Information resources (General) Value (computer science) computer.software_genre lcsh:QA75.5-76.95 Cluster (physics) Data mining lcsh:Electronic computers. Computer science Cluster analysis education Baseline (configuration management) computer Self organized map lcsh:ZA3040-5185 |
| Zdroj: | Knowledge Engineering and Data Science, Vol 2, Iss 1, Pp 31-40 (2019) |
| ISSN: | 2597-4637 2597-4602 |
| 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: | OpenAIRE |
| Externí odkaz: |
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