Comparison analysis of Euclidean and Gower distance measures on k-medoids cluster

Aditya, Agil; Sari, Betha Nurina; Padilah, Tesa Nur

doi:10.14710/jtsiskom.2020.13747

Cited by 8 publications

(6 citation statements)

References 18 publications

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“…Pada penelitian ini melakukan improvisasi pada algoritma K-means dengan metode Euclidean dan mendapatkan hasil pengujian dengan akurasi 80,57% pada Sembilan dataset mutivariat [9]. Perbandingan metode Euclidean dan Gower dengan menggunakan tujuh dataset berbeda yaitu data pelanggan grosir, data hepatitis, kontrasepsi, ujian nasional sekolah menengah pertama, penyakit diare, campak, dan data tenaga kesehatan, metode yang digunakan untuk pengujian adalah Silhouette pada metode Euclidean 0.8121 dan metode Gower mendapatkan 0.8513 pada dataset yang sama [10]. Penerapan K-Means dengan perhitungan jarak Manhattan memiliki kinerja yang baik dalam lingkungan big data [11].…”

Section: Pendahuluanunclassified

Komparasi Metode Perhitungan Jarak K-Means Paling Baik Terhadap Pembentukan Pola Kunjungan Wisatawan Mancanegara

Mutawalli,

Fadli,

Supardianto

2023

josh

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Understanding patterns among foreign tourists is an urgent matter. These patterns can become knowledge that helps in making better decisions because they are data-driven. The pattern to be elaborated on is regarding the clustering of visits by foreign tourists to tourist destinations in Jakarta. Data mining is an approach that extracts knowledge patterns from a dataset. K-Means is one of the data mining algorithms used for clustering data, where data is grouped based on similarity in features and attributes. This study compares the Euclidean Distance, Manhattan Distance, and Haversine Distance methods to obtain more representative data clusters for the datasets. The datasets in this study are not normally distributed due to outlier data; hence, the DBSCAN algorithm is used for improvement without removing or cutting the data, as it can result in a significant amount of missing values that could affect information that does not align with empirical facts. In this study, 5 clusters were created based on elbow calculation results. The K-Means cluster testing in Euclidean distance yielded a Silhouette Score of 0.36, Inertia of 0.86, and Davies-Bouldin Index of 2.39. The Manhattan method resulted in a Silhouette Score of 0.65, Inertia of 1.46, and Davies-Bouldin Index of 0.47. Meanwhile, applying the Haversine method resulted in a Silhouette Score of 0.36, Inertia of 0.03, and a value of 2.39 for the Davies-Bouldin Index.

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Section: Pendahuluanunclassified

Komparasi Metode Perhitungan Jarak K-Means Paling Baik Terhadap Pembentukan Pola Kunjungan Wisatawan Mancanegara

Mutawalli,

Fadli,

Supardianto

2023

josh

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“…The nearest neighbor search technique is generally carried out using the Euclidean distance formula. Equation ( 1) below shows the calculation formula for finding the shortest distance using the Euclidean formula [21].…”

Section: B K-nearest Neighbormentioning

confidence: 99%

Implementation of K-Nearest Neighbor for Fall Position Detection of Dementia Patients Based Microcontroller

Yulastri,

Madona,

Devy

et al. 2023

JECCOM

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A microcontroller-based detection tool for the presence of patients with dementia has been made using the K-Nearest Neighbor (KNN) method with the help of coordinate points that can be seen via Google Maps. which is based on patient care with a patient-oriented approach. The targets of this research are (a) designing and implementing a fall detection system using the mpu6050 sensor, (b) using the (KNN) method to determine the coordinates of the location of dementia patients using GPS. The research method starts from making a prototype and measuring system performance. The test results on GPS produced an average latitude error of 0.002091% and an average longitude error of 0.000032% in Pauh District, while in Lubuk Kilangan District the average latitude error was 0.002641% and an average longitude error of 0.000150%. The KNN method with the Eucledian distance formula can help supervisors find out the nearest police station to the patient through the coordinate points detected by GPS by taking the smallest value from the comparison of values in the form of degrees between the Pauh police station and the Lubuk Kilangan police station for the patient. Overall the tool can function well.

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“…n out of X data are chosen as the initial cluster centers; -The Euclidean distance [16] is used to compute the distance (similarity) between each object and the n centers. The formula is defined as:…”

Section: Cluster Analysismentioning

confidence: 99%

Impact of regional haze pollution and local meteorological conditions on urban residential areas

Kang,

Chen

2023

Sust. Build.

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With the accelerated process of economic construction and urbanization, motor vehicles and pollutants emitted from winter heating are constantly increasing, and regional haze pollution caused by these factors has affected people's living environment. This article took Nanning City, Guangxi Province, as the research area and analyzed the relationship between regional haze pollution and local meteorology by using the Pearson correlation coefficient. Then, cluster analysis was carried out to find out the main impacts of these two factors on urban residential areas. The results showed that among the local meteorological factors, average precipitation was strongly correlated with the occurrence of haze pollution, while other meteorological factors showed a very strong correlation. According to the results of cluster analysis, the impact of regional haze pollution and local meteorology on urban residential areas was mainly divided into three categories: reduced visibility, reduced human health, and reduced living comfort. The largest impact was on reduced visibility, and 79.11% of the total population felt that the visibility was affected.

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Comparison analysis of Euclidean and Gower distance measures on k-medoids cluster

Cited by 8 publications

References 18 publications

Komparasi Metode Perhitungan Jarak K-Means Paling Baik Terhadap Pembentukan Pola Kunjungan Wisatawan Mancanegara

Komparasi Metode Perhitungan Jarak K-Means Paling Baik Terhadap Pembentukan Pola Kunjungan Wisatawan Mancanegara

Implementation of K-Nearest Neighbor for Fall Position Detection of Dementia Patients Based Microcontroller

Impact of regional haze pollution and local meteorological conditions on urban residential areas

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