Simple K-Medoids Partitioning Algorithm for Mixed Variable Data

Budiaji, Weksi; Leisch, Friedrich

doi:10.3390/a12090177

Cited by 45 publications

(27 citation statements)

References 25 publications

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“…Penerapan algoritma K-Medoids untuk clustering pada FP Growth dalam menghasilkan aturan rekomendasi produk pada jumlah yang besar, dari dataset sehingga dapat memberikan rekomendasi teknis / baru cara ke 212 Mart dalam menentukan promosi produk [10]. Penerapan algoritma K-Medoids untuk meningkakan performasi algoritma SFKM [11]. Penerapan algoritma K-Medoids dalam pengelompokan WhatsApp, Moodle, dan Zoom untuk proses pembelajaran.…”

Section: Pendahuluanunclassified

Klasterisasi Teknik Promosi dalam Meningkatkan Mutu Kampus Menggunakan Algoritma K-Medoids

Darma

Nurcahyo²

2021

INFEB

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Promotion technique is one of the methods used by AMIK and STIKOM Tunas Bangsa Pematangiantar in finding new students. By developing promotional techniques that are more effective and efficient, it can produce the information needed to get new student candidates. The data that is processed in this research is the data of new student candidates who come from the AMIK New Student Admissions (PMB) and STIKOM Tunas Bangsa Pematangsiantar. Based on promotional data in 2020, there are several promotions carried out, including through print media, banners, collaboration with SMA / SMK schools in Pematangsiantar city, through alumni, and the web. Furthermore, the data is processed using Rapidminer software. The processing stages are grouped using the K-Medoids algorithm on the data of prospective students, including name, place and date of birth, address, religion, cellphone number, school origin, choice of study program and source of information. Followed by the process of improving the data so that more accurate data is obtained to be processed. The results of the testing of this method are knowing which regions are applying to AMIK and STIKOM Tunas Bangsa Pematngsiantar which are grouped into 2 clusters, namely the highest and the lowest. The K-Medoids algorithm that is used makes a big contribution by providing new information that can be used as a reference for AMIK and STIKOM Tunas Bangsa Pematngsiantar in terms of Promoting New Student Admissions in the coming year.

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Section: Pendahuluanunclassified

Klasterisasi Teknik Promosi dalam Meningkatkan Mutu Kampus Menggunakan Algoritma K-Medoids

Darma

Nurcahyo²

2021

INFEB

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“…[5] menerapkan algoritme k-medoids dan k-means dalam klastering wilayah gempa bumi di Indonesia dan menunjukkan bahwa algoritme k-medoids memberikan nilai Silhouette yang lebih baik daripada kmeans. Di sisi lain, k-medoids menghasilkan komputasi yang baik [6] dan konsisten dalam berbagai pengaturan data variabel campuran [7]. K-medoids dapat melakukan pengelompokan menggunakan objek yang paling terpusat (medoids) menjadi sejumlah k klaster dari nilai rata-rata objek dalam sebuah klaster.…”

Section: Pendahuluanunclassified

“…Performa hasil klaster k-medoids berhubungan erat dengan teknik pengukuran jarak yang digunakan. Beberapa metode pengukuran jarak yang dapat digunakan adalah Euclidean, Manhattan, Chebyshev, Minkowski, dan Gower [7], [11]- [13]. Hal ini menunjukkan bahwa penentuan metode pengukuran jarak adalah hal yang penting dilakukan dalam klastering menggunakan kmedoids.…”

Section: Pendahuluanunclassified

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

Aditya

Sari

Padilah

2020

J. Teknol. dan Sist. Komput

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K-medoids clustering uses distance measurement to find and classify data that have similarities and inequalities. The distance measurement method selection can affect the clustering performance for a dataset. Several studies use the Euclidean and Gower distance as measurement methods in numerical data clustering. This study aims to compare the performance of the k-medoids clustering on a numerical dataset using the Euclidean and Gower distance. This study used seven numerical datasets and Silhouette, Dunn, and Connectivity indexes in the clustering evaluation. The Euclidean distance is superior in two values of Silhouette and Connectivity indexes so that Euclidean has a good data grouping structure, while the Gower is superior in Dunn index showing that the Gower has better cluster separation compared to Euclidean. This study shows that the Euclidean distance is superior to the Gower in applying the k-medoids algorithm with a numeric dataset.

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“…Specifically, the proposed algorithm consists of two stages; an offline stage and an online stage. In the offline stage, OFCOD employs the K-Medoids clustering algorithm [30,31]. A proposed enhancement is presented to reduce the distance calculations performed by K-Medoids to avoid repeated/redundant distance calculations and optimize the utilization of the computer memory.…”

Section: Introductionmentioning

confidence: 99%

OFCOD: On the Fly Clustering Based Outlier Detection Framework

Elmogy

Rizk

Sarhan

2020

Data

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In data mining, outlier detection is a major challenge as it has an important role in many applications such as medical data, image processing, fraud detection, intrusion detection, and so forth. An extensive variety of clustering based approaches have been developed to detect outliers. However they are by nature time consuming which restrict their utilization with real-time applications. Furthermore, outlier detection requests are handled one at a time, which means that each request is initiated individually with a particular set of parameters. In this paper, the first clustering based outlier detection framework, (On the Fly Clustering Based Outlier Detection (OFCOD)) is presented. OFCOD enables analysts to effectively find out outliers on time with request even within huge datasets. The proposed framework has been tested and evaluated using two real world datasets with different features and applications; one with 699 records, and another with five millions records. The experimental results show that the performance of the proposed framework outperforms other existing approaches while considering several evaluation metrics.

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Simple K-Medoids Partitioning Algorithm for Mixed Variable Data

Cited by 45 publications

References 25 publications

Klasterisasi Teknik Promosi dalam Meningkatkan Mutu Kampus Menggunakan Algoritma K-Medoids

Klasterisasi Teknik Promosi dalam Meningkatkan Mutu Kampus Menggunakan Algoritma K-Medoids

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

OFCOD: On the Fly Clustering Based Outlier Detection Framework

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