Comparison of Clustering Algorithms: Fuzzy C-Means, K-Means, and DBSCAN for House Classification Based on Specifications and Price
This study aims to compare the performance of three clustering algorithms, namely Fuzzy C-Means, K-Means, and DBSCAN, in grouping houses based on their specifications and prices. The data used includes features such as price, building area, land area, number of bedrooms, number of bathrooms, and ava...
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| Language: | English |
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Politeknik Negeri Batam
2024-11-01
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| Series: | Journal of Applied Informatics and Computing |
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| Online Access: | https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/8671 |
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| author | Dhendy Mardiansyah Putra Ferian Fauzi Abdulloh |
| author_facet | Dhendy Mardiansyah Putra Ferian Fauzi Abdulloh |
| author_sort | Dhendy Mardiansyah Putra |
| collection | DOAJ |
| description | This study aims to compare the performance of three clustering algorithms, namely Fuzzy C-Means, K-Means, and DBSCAN, in grouping houses based on their specifications and prices. The data used includes features such as price, building area, land area, number of bedrooms, number of bathrooms, and availability of garages. The performance of these algorithms was evaluated using Silhouette Score and Davies-Bouldin Score to determine the quality of cluster separation. The results indicate that K-Means achieved the best performance with the highest Silhouette Score of 0.7702 for two clusters, followed by Fuzzy C-Means, which excelled in handling overlapping clusters. DBSCAN, while effective in detecting outliers, showed suboptimal performance for this housing dataset. These findings suggest that K-Means is the most suitable clustering method for housing data, while Fuzzy C-Means and DBSCAN can serve as alternatives depending on the data characteristics. This research is expected to assist in making the house searching and classification process more efficient and provide additional insights for developers in shaping housing market strategies. |
| format | Article |
| id | doaj-art-e5cbb8adc46d4171af5a10f59f03888e |
| institution | OA Journals |
| issn | 2548-6861 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Politeknik Negeri Batam |
| record_format | Article |
| series | Journal of Applied Informatics and Computing |
| spelling | doaj-art-e5cbb8adc46d4171af5a10f59f03888e2025-08-20T01:55:18ZengPoliteknik Negeri BatamJournal of Applied Informatics and Computing2548-68612024-11-018250951510.30871/jaic.v8i2.86718671Comparison of Clustering Algorithms: Fuzzy C-Means, K-Means, and DBSCAN for House Classification Based on Specifications and PriceDhendy Mardiansyah Putra0Ferian Fauzi Abdulloh1Universitas Amikom YogyakartaUniversitas Amikom YogyakartaThis study aims to compare the performance of three clustering algorithms, namely Fuzzy C-Means, K-Means, and DBSCAN, in grouping houses based on their specifications and prices. The data used includes features such as price, building area, land area, number of bedrooms, number of bathrooms, and availability of garages. The performance of these algorithms was evaluated using Silhouette Score and Davies-Bouldin Score to determine the quality of cluster separation. The results indicate that K-Means achieved the best performance with the highest Silhouette Score of 0.7702 for two clusters, followed by Fuzzy C-Means, which excelled in handling overlapping clusters. DBSCAN, while effective in detecting outliers, showed suboptimal performance for this housing dataset. These findings suggest that K-Means is the most suitable clustering method for housing data, while Fuzzy C-Means and DBSCAN can serve as alternatives depending on the data characteristics. This research is expected to assist in making the house searching and classification process more efficient and provide additional insights for developers in shaping housing market strategies.https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/8671clusteringk-meansfuzzy c-meansdbscanhousing classificationreal estate analysis |
| spellingShingle | Dhendy Mardiansyah Putra Ferian Fauzi Abdulloh Comparison of Clustering Algorithms: Fuzzy C-Means, K-Means, and DBSCAN for House Classification Based on Specifications and Price Journal of Applied Informatics and Computing clustering k-means fuzzy c-means dbscan housing classification real estate analysis |
| title | Comparison of Clustering Algorithms: Fuzzy C-Means, K-Means, and DBSCAN for House Classification Based on Specifications and Price |
| title_full | Comparison of Clustering Algorithms: Fuzzy C-Means, K-Means, and DBSCAN for House Classification Based on Specifications and Price |
| title_fullStr | Comparison of Clustering Algorithms: Fuzzy C-Means, K-Means, and DBSCAN for House Classification Based on Specifications and Price |
| title_full_unstemmed | Comparison of Clustering Algorithms: Fuzzy C-Means, K-Means, and DBSCAN for House Classification Based on Specifications and Price |
| title_short | Comparison of Clustering Algorithms: Fuzzy C-Means, K-Means, and DBSCAN for House Classification Based on Specifications and Price |
| title_sort | comparison of clustering algorithms fuzzy c means k means and dbscan for house classification based on specifications and price |
| topic | clustering k-means fuzzy c-means dbscan housing classification real estate analysis |
| url | https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/8671 |
| work_keys_str_mv | AT dhendymardiansyahputra comparisonofclusteringalgorithmsfuzzycmeanskmeansanddbscanforhouseclassificationbasedonspecificationsandprice AT ferianfauziabdulloh comparisonofclusteringalgorithmsfuzzycmeanskmeansanddbscanforhouseclassificationbasedonspecificationsandprice |