Application Of K-means Algorithm Research Articles

Application of k-Means Algorithm on Clustering Poor Population Data for Extreme Poverty Elimination

Application of k-Means Algorithm on Clustering Poor Population Data for Extreme Poverty Elimination

Application of K-Means Algorithm in Marketing

The purpose of this paper is to explore the application of K-means cluster analysis in the field of marketing. Firstly, this paper briefly introduces the research background, the research topic, and the significance of the research. K-means is an important algorithm in data mining, so this paper first gives a brief overview of data mining and introduces its main tasks. Then it focuses on explaining the related knowledge of the K-means algorithm, which includes the specific principles, advantages, improvements, and dissimilarity calculation of the K-means algorithm. In the specific combination of marketing, how to use K-means for clustering is described in detail using consumers as an example. Using the literature research method, this paper concludes that K-means cluster analysis is a very useful tool in marketing, which can help companies better understand their markets and customers, and develop more accurate marketing strategies.

Application of K-Means Algorithm on Clustering Recipients of Non-Cash Food Assistance (NCFA)

Persoalan Kemiskinan pada berbagai daerah Indonesia menjadi fokus perhatian. Program BPNT (Bantuan Pangan Non Tunai) bermaksud memangkas biaya pangan dan membagikan gizi yang sepadan terhadap KPM (Keluarga Penerima Manfaat). Penelitian ini menerapkan algoritma K-Means untuk menganalisis pola karakteristik penerima BPNT di Pekanbaru. Data yang digunakan berasal dari penelitian sebelumnya oleh Firza Syahputra dan dari Dinas Sosial Kota Pekanbaru tahun 2020-2021 dengan 732 data dan 41 parameter. Penerapan K-Means dilakukan melalui Google Colab. Melalui data mining dan metode clustering, ditemukan dua klaster dengan 666 data dalam klaster 1 dan 16 data dalam klaster 2. Evaluasi menggunakan Silhouette Score menunjukkan hasil yang baik, dengan nilai 0.9169796594018274. Penelitian ini berpotensi membantu pemerintah dalam mengambil keputusan yang efektif selama penyebaran bantuan pangan non tunai kepada rakyat yang membutuhkan. Dengan demikian, algoritma K-Means Clustering dapat mengidentifikasi pola karakteristik penerima BPNT dan membedakan kelompok yang layak dan tidak layak menerima bantuan.Poverty issues in various parts of Indonesia are the focus of attention. The NCFA (Non-Cash Food Assistance) program's purpose are to lower food consumption and give Beneficiary Families (BF) a healthy diet. The k-means technique use in this study to assess the distinctive patterns of NCFA grantees in Pekanbaru. The data used comes from previous research by Firza Syahputra and from Social Affairs Office Pekanbaru in 2020-2021 with 732 data and 41 parameters. The application of k-means is done through Google Colab. Through data mining and clustering methods, two clusters were found with 666 data in cluster 1 and 16 data in cluster 2. Evaluation using Silhouette Score showed good results, with a value of 0.9169796594018274. This research has the potential to assist the government in making effective decisions in distributing non-cash food help people in need. For the result, the k-means Clustering technique is able to recognize the traits of NCFA recipients and identify groups that are and are not eligible for aid.

Application of K-means algorithm in amino acid data set of different jujube cultivars

Application of K-means algorithm in amino acid data set of different jujube cultivars

ANALYSIS AND VISUALIZATION OF MARKET SEGEMENTATION IN BANKING SECTOR USING KMEANS MACHINE LEARNING ALGORITHM

Segmentation is a way of assigning each dataset to a segment called cluster. It is widely applied in different area of human endeavor such as banking sector, health sector, retail, media etc. Many organizations are faced with problems of ineffective customer care services and intelligent management decisions because of inability to effectively analyze customer data that will give insight to the nature of customers to help in effective customer services and intelligent management decision. Kmeans algorithm is the widely used algorithm for market segmentation, normally the k value of Kmeans algorithm are randomly picked. Picking the optimal k value is usually a challenge in application of Kmeans algorithm and this usually affects the performance of Kmeans algorithm. This work applies elbow method to obtain the optimal k value that was applied to analyze dataset from banking sector (in this case United Bank of Africa) for better insight, business management and marketing strategy. The customer cluster created was evaluated using visual plots and cluster centers. The optimal k value of six (6) was obtained using the elbow function. The dataset was thus segmented based on the optimal k value of 6 obtained. The clustering results obtained showed high intra cluster similarity (data within a cluster are similar) and low inter cluster similarity (data from different clusters are dissimilar). The result also showed that customers in cluster 3 and 4 has similar marketing needs and can be served together

Application of K-Means Algorithm in Grouping Households Accessing the Internet by Province

The study aims to group households that access the Internet according to the province. As for the data source in this study was made from BPS (the statistical center body) and the data used in the study was in 2017 to 2019 isolated from 34 provinces in Indonesia. The method of artificially synthesizing the research is using a k-means algorithm. According to the data, groups of households that access the Internet according to the provinces are grouped into 2 clusters of high clusters (c1) and low cluster (c2). It is hoped that this study will provide more attention to the government for the provinces that have low Internet access, It could also lead to programs that would seek to improve people's access to the Internet via e-government, telencenter, smart villages, or smart city, and indonesian-to pursue their relationship with more advanced Internet countries such as Europe and America

AKADEMIK DATA MINING (ADM) K-MEANS DAN K-MEANS K-NN UNTUK MENGELOMPOKAN KELAS MATA KULIAH KOSENTRASI MAHASISWA SEMESTER AKHIR

University as an educational institution plays an important role in producing graduates. In addition, institutions such as universitas ichsan Gorontalo save the data set. These Data include about student academic data.In the academic field, every semester, increasing the amount of data recorded with data from academic activities. It is like there is a Tsunami of data which indicate that these data are very abundant but do not give any knowledge that is not beneficial to the university, especially the faculty except the knowledge administrative. Universitas ichsan Gorontalo with the number of students reached 9000 people which is accompanied by the number of graduates is still less than ideal any period graduate, it is necessary to apply the pattern determination grade concentration courses effective for the achievement ability of students, academic Data will be used namely the data of the students 2016-2017 who has taken class subjects concentration. The application of K-Means algorithm and K-Means KNN where K=2 result in a cluster for grouping of a Class Focus on the students semester end and each cluster has a predictive value for the second klustering such, the Value of the resulting Accuracy of Algorithms KNN, namely the AUC (Area Under The Curve) =1, the Value of CA=1, the value of F1=1, the value of the precision=1 and recall=1, and the value of accuracy as the best value.

Application of K-means Algorithm in Image Compression

In machine learning problems, there are two main algorithms: supervised learning and unsupervised learning. Supervised learning algorithms can be used to classify data for tagged data; non-supervised learning algorithms can be used to cluster data for unlabeled data. This paper discusses the basic principles of clustering algorithm and selection of key parameters of clustering algorithm. The application of clustering algorithm in image compression is also analyzed. This paper also emphasizes the problems that should be paid attention to when using clustering. Finally, a practical case of image compression with K-means is given.

Application of K-means Algorithm in Geological Disaster Monitoring System

Abstract The K-means algorithm is considered to be the most important unsupervised machine learning method in clustering, which can divide all the data into k subclasses that are very different from each other. As K-means algorithm is simple and efficient, it is applied to data mining, knowledge discovery and other fields. This paper proposes CMU-kmeans algorithm with improved UPGMA algorithm and Canopy algorithm. The experimental results is that the algorithm can not only get the number k of the initial clustering center adaptable, but also avoid the influence of the noise data and the edge data. Also, the improved algorithm can void the initial effect of the random selection on the clustering, which reflects the actual distribution in the dataset.

Application of K-Means Algorithm with Person Coefficient in Colleges Physical Education

For developing students' personality and interest of participating in physical exercise, improving students physical fitness, schools at all levels have carried out useful Physical Education exploration and reform. However, by the restrictions of teaching management system, physical education teachers and teaching venues and facilities, the students' physical condition is not improved ideally. Under existing conditions, how to use Pearson coefficient K-means algorithm to optimize physical education according to the students 'sports quality has become a topic worthy of study. It can guide how to set the physical education curriculum and teaching content, so as to promote the healthy development of students' physical fitness.

Application of Kmeans Algorithm with Pearson Coefficient in College Student Sports Quality and Sports Resources

Because the higher education has entered popularization stage, the contradiction between the unlimited demand for higher education resources of colleges and universities and the limited supply of resources becomes more and more prominent. Similarly, college sports resources is also limited. How to optimize the sports curriculum according to the students quality is a subject worthy of study. This paper proposes to study the students sport quality such as vital capacity, grip strength, enduranceuse using Pearson correlation and Kmeans clustering algorithm. It can provide guidance for sports education curriculum and teaching content. And we use 2013 freshmen sports test data of an university as example, the experiment proves that the present algorithm can effectively guide for curriculum set.