Home appliances recommendation system based on weather information using combined modified k-means and elbow algorithms
The recommendation system is an intelligent system gives recommendations to users to discover the best interesting items. The purpose of this proposed recommendation system is to develop a system to find the best electrical devices according to weather conditions and user preferences. The proposed s...
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| Main Authors: | , , |
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2020-09-01.
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| Online Access: | Get fulltext |
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| LEADER | 02672 am a22003133u 4500 | ||
|---|---|---|---|
| 001 | ijeecs21743_14147 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Amer Jaafar, Basim |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Talib Gaata, Methaq |e author |
| 700 | 1 | 0 | |a Nsaif Jasim, Mahdi |e author |
| 245 | 0 | 0 | |a Home appliances recommendation system based on weather information using combined modified k-means and elbow algorithms |
| 260 | |b Institute of Advanced Engineering and Science, |c 2020-09-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/21743 | ||
| 520 | |a The recommendation system is an intelligent system gives recommendations to users to discover the best interesting items. The purpose of this proposed recommendation system is to develop a system to find the best electrical devices according to weather conditions and user preferences. The proposed solution relies on the characteristics of electrical appliances and their suitability to weather conditions in any city. The proposed solution is the first recommendation system combines devices properties, weather conditions, and user preferences using a new combination of algorithms. The clustering algorithms are the most applicable in the field of recommendation system. The proposed solution relies on a combination of Elbow method, proposed modified K-means and Silhouette algorithm to find the best number of clusters before starting the clustering process. Then calculate the weights for each cluster and compare them with the weather weights to find the required clusters sorted from the near to far according to a computed threshold. The empirical results showed that the proposed solution demonstrated a 94% accuracy to match the characteristics of the recommended devices with the climatic characteristics of the region and user preferences. The accuracy is measured using Silhouette algorithm. | ||
| 540 | |a Copyright (c) 2020 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |||
| 690 | |a Clustering; Elbow method; K-means; Optimal number of clusters; Recommendation system | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 19, No 3: September 2020; 1635-1642 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v19.i3 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/21743/14147 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/21743/14147 |z Get fulltext |