Data clustering method based on ant swarm intelligence

Swarm intelligence has appeared as an active field for solving numerous machine-learning tasks. In this paper, we address the problem of clustering data with missing values, where the patterns are described by mixed (or hybrid) features. We introduce a generic modification to three swarm intelligence algorithms (Artificial Bee Colony, Firefly Algorithm, and Novel Bat Algorithm). We experimentally obtain the adequate values of the parameters for these three modified algorithms, with the purpose of applying them in the clustering task. We also provide an unbiased comparison among several metaheuristics based clustering algorithms, concluding that the clusters obtained by our proposals are highly representative of the “natural structure” of data.

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KOMPARASI METODE CLUSTERING K-MEANS DAN K-MEDOIDS DENGAN MODEL FUZZY RFM UNTUK PENGELOMPOKAN PELANGGAN

Evolusi : Jurnal Sains dan Manajemen ◽

10.31294/evolusi.v6i2.4600 ◽

2018 ◽

Vol 6 (2) ◽

Author(s):

Elly Muningsih - AMIK BSI Yogyakarta

Keyword(s):

Data Clustering ◽

Small Data ◽

Clustering Methods ◽

Monetary Model ◽

Clustering Method ◽

Online Sales ◽

Rfm Model ◽

Potential Customers ◽

Cluster 2 ◽

Better Than

Abstract ~ The K-Means method is one of the clustering methods that is widely used in data clustering research. While the K-Medoids method is an efficient method used for processing small data. This study aims to compare two clustering methods by grouping customers into 3 clusters according to their characteristics, namely very potential (loyal) customers, potential customers and non potential customers. The method used in this study is the K-Means clustering method and the K-Medoids method. The data used is online sales transaction. The clustering method testing is done by using a Fuzzy RFM (Recency, Frequenty and Monetary) model where the average (mean) of the third value is taken. From the data testing is known that the K-Means method is better than the K-Medoids method with an accuracy value of 90.47%. Whereas from the data processing carried out is known that cluster 1 has 16 members (customers), cluster 2 has 11 members and cluster 3 has 15 members. Keywords : clustering, K-Means method, K-Medoids method, customer, Fuzzy RFM model. Abstrak ~ Metode K-Means merupakan salah satu metode clustering yang banyak digunakan dalam penelitian pengelompokan data. Sedangkan metode K-Medoids merupakan metode yang efisien digunakan untuk pengolahan data yang kecil. Penelitian ini bertujuan untuk membandingkan atau mengkomparasi dua metode clustering dengan cara mengelompokkan pelanggan menjadi 3 cluster sesuai dengan karakteristiknya, yaitu pelanggan sangat potensial (loyal), pelanggan potensial dan pelanggan kurang (tidak) potensial. Metode yang digunakan dalam penelitian ini adalah metode clustering K-Means dan metode K-Medoids. Data yang digunakan adalah data transaksi penjualan online. Pengujian metode clustering yang dilakukan adalah dengan menggunakan model Fuzzy RFM (Recency, Frequenty dan Monetary) dimana diambil rata-rata (mean) dari nilai ketiga tersebut. Dari pengujian data diketahui bahwa metode K-Means lebih baik dari metode K-Medoids dengan nilai akurasi 90,47%. Sedangkan dari pengolahan data yang dilakukan diketahui bahwa cluster 1 memiliki 16 anggota (pelanggan), cluster 2 memiliki 11 anggota dan cluster 3 memiliki 15 anggota. Kata kunci : clustering, metode K-Means, metode K-Medoids, pelanggan, model Fuzzy RFM.

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A Max-Min clustering method for $k$-means algorithm of data clustering

Journal of Industrial and Management Optimization ◽

10.3934/jimo.2012.8.565 ◽

2012 ◽

Vol 8 (3) ◽

pp. 565-575 ◽

Cited By ~ 2

Author(s):

Yubo Yuan ◽

Wanjun Zhang ◽

Baolan Yuan

Keyword(s):

Data Clustering ◽

Clustering Method

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FUZZY CLUSTERING MEANS (FCM) DALAM PENENTUAN LOKASI PENERTIBAN PENYAKIT MASYARAKAT PADA KEGIATAN PEMBINAAN SOSIAL SATPOL-PP WILAYAH SUMATRA-BARAT

Petir ◽

10.33322/petir.v10i1.33 ◽

2018 ◽

Vol 10 (1) ◽

Author(s):

Redaksi Tim Jurnal

Keyword(s):

Fuzzy Clustering ◽

Community Policing ◽

Sex Workers ◽

Data Clustering ◽

Behavior Patterns ◽

Clustering Method ◽

Street Vendors ◽

West Sumatra ◽

Municipal Police ◽

Disease Community

Based on the data summary disease community policing activities by municipal police pp city of West Sumatra in January 2010 to December 2014, there were as many as 1660 cases of approximately 20 locations enforcement. Each location policing there are various types of activities are classified as a disease of society. Based on data obtained are activities that have curbed such as street vendors, illegal buildings, street children, street, commercial sex workers (CSWs) and others. Number of activities at each point different locations each year, thus requiring data clustering method to facilitate the investigation team in determining the behavior patterns of disease activity as a description of the location community policing a priority next year. The method used in this data clustering method is to use Fuzzy Clustering Means (FCM)

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