scholarly journals Analisis Clustering Pengelompokan Penjualan Paket Data Menggunakan Metode K-Means

2021 ◽  
Vol 13 (1) ◽  
pp. 33-38
Author(s):  
Dimas Galang Ramadhan ◽  
Indri Prihatini ◽  
Febri Liantoni
Keyword(s):  
Cluster Analysis ◽  
Cluster Center ◽  
Active Period ◽  
Analysis Algorithm ◽  
Initial Cluster

At present with the COVID-19 pandemic situation that requires all activities based in the network, starting from work, college, school, everything is based on the network. Certain provider users will experience excessive data plan usage. This also has an effect on a counter that sells data packages, which must provide several data package services in accordance with current conditions. This research was conducted to analyze the grouping of sales of data packages that are often purchased by customers in a counter by using the K-Means method. The K-Means method is used because the K-Means algorithm is not influenced by the order of the objects used, this is proven when the writer tries to determine the initial cluster center randomly from one of the objects in the first calculation. sales of data packages at a counter. Variables used include Price, Active period, and number of data packages. The K-Means Cluster Analysis algorithm is basically applied to the problem of understanding consumer needs, identifying the types of data package products that are often purchased. The K-Means algorithm can be used to describe the characteristics of each group by summarizing a large number of objects so that it is easier.

scholarly journals Hybrid K Mean Clustering Algorithm for Crop Production Analysis in Agriculture

2019 ◽  
Vol 9 (2S) ◽  
pp. 9-13
Keyword(s):  
Cluster Analysis ◽  
Precision Agriculture ◽  
Crop Production ◽  
Clustering Algorithm ◽  
Research Work ◽  
K Value ◽  
Clustering Technique ◽  
Production Analysis ◽  
Initial Cluster ◽  
The Stability

The proposed research work aims to perform the cluster analysis in the field of Precision Agriculture. The k-means technique is implemented to cluster the agriculture data. Selecting K value plays a major role in k-mean algorithm. Different techniques are used to identify the number of cluster value (k-value). Identification of suitable initial centroid has an important role in k-means algorithm. In general it will be selected randomly. In the proposed work to get the stability in the result Hybrid K-Mean clustering is used to identify the initial centroids. Since initial cluster centers are well defined Hybrid K-Means acts as a stable clustering technique.

scholarly journals Weighted k-Prototypes Clustering Algorithm Based on the Hybrid Dissimilarity Coefficient

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Ziqi Jia ◽  
Ling Song
Keyword(s):  
Categorical Data ◽  
Clustering Algorithm ◽  
Numerical Data ◽  
Experimental Results ◽  
Cluster Center ◽  
Real Dataset ◽  
Dissimilarity Coefficient ◽  
Initial Cluster ◽  
Data Objects ◽  
Selection Of

The k-prototypes algorithm is a hybrid clustering algorithm that can process Categorical Data and Numerical Data. In this study, the method of initial Cluster Center selection was improved and a new Hybrid Dissimilarity Coefficient was proposed. Based on the proposed Hybrid Dissimilarity Coefficient, a weighted k-prototype clustering algorithm based on the hybrid dissimilarity coefficient was proposed (WKPCA). The proposed WKPCA algorithm not only improves the selection of initial Cluster Centers, but also puts a new method to calculate the dissimilarity between data objects and Cluster Centers. The real dataset of UCI was used to test the WKPCA algorithm. Experimental results show that WKPCA algorithm is more efficient and robust than other k-prototypes algorithms.

Study of the Cluster Analysis Algorithm for Radio Advertising

2015 ◽  
Vol 713-715 ◽  
pp. 1935-1938
Author(s):  
Ji Hong Liu ◽  
Yu Tao Fu ◽  
Qi Zhang ◽  
Yu Ting Geng
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Analysis Algorithm ◽  
Design And Implementation ◽  
Key Technologies ◽  
Audio Data

Some key technologies for clustering the radio advertising are introduced firstly. Then the design and implementation of the system are presented. The system analyzes the cluster characters for radio advertising by principal component analysis. It could be used to capture the radio ads’ time slots. This system shows a way to analyze audio data, and could be used to classify and identify different audio ads. Therefore, it has a wonderful application prospect.

scholarly journals Noise Rejection Approaches for Various Rough Set-Based C-Means Clustering

2020 ◽  
Vol 24 (6) ◽  
pp. 738-749
Author(s):  
Seiki Ubukata ◽  
◽  
Sho Sekiya ◽  
Akira Notsu ◽  
Katsuhiro Honda
Keyword(s):  
Cluster Analysis ◽  
Robust Estimation ◽  
Rough Set ◽  
Real World ◽  
Numerical Experiments ◽  
Cluster Center ◽  
Two Dimensional ◽  
Noise Rejection ◽  
Noise Clustering ◽  
Real World Datasets

In the field of cluster analysis, rough set-based extensions of hard C-means (HCM; k-means) including rough C-means (RCM), rough set C-means (RSCM), and rough membership C-means (RMCM) are promising approaches for dealing with the certainty, possibility, uncertainty of belonging of object to clusters. Since C-means-type methods are strongly affected by noise, noise clustering approaches have been proposed. In noise clustering approaches, noise objects, which are far from any cluster center, are rejected for robust estimation. In this paper, we introduce noise rejection approaches for rough set-based C-means based on probabilistic memberships and propose noise RCM with membership normalization (NRCM-MN), noise RSCM with membership normalization (NRSCM-MN), and noise RMCM (NRMCM). In addition, visualization demonstration of the cluster boundaries on the two-dimensional plane of the proposed methods is carried out to confirm the characteristics of each method. Furthermore, the clustering performance is verified by numerical experiments using real-world datasets.

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