Application Of K-means Clustering Algorithm Research Articles

The Application of K-means Clustering Algorithm in the Evaluation of E-Commerce Websites

When dealing with large amounts of high-dimensional transaction data, clustering approaches often struggle with challenges including elasticity, weak processing capabilities of high-dimensional data, sensitivity to data sequence across time, independence of parameters, and ability to manage noise. These problems may prevent the methods from providing accurate predictions. Experiments conducted with data samples collected from 300 different mobile phones purchased on Taobao yielded the following results. K-means beats Single-pass in evaluating e-commerce transactions because of its higher intra-class dissimilarity and inter-class similarity. K-means clustering is an approach to the organization of massive datasets that is both effective and flexible. The outcome of the clustering algorithm is sensitive not only to the total number of clusters but also to how they were initially arranged. Because of this, it is simple to demonstrate that the results of clustering are best optimized locally. For this reason, continuing research into the elements that influence the number of clusters produced by this method as well as the starting locations of the clustering center is a crucial endeavor.

Application of K-Means Clustering Algorithm to Obtain Recommendations for Strategies to Increase the Number of Students in the Information Systems Study Program at ITB Ahmad Dahlan Jakarta

The rapid development of technology today has almost touched all sectors of life such as the economy, health and education. The technology currently used produces a lot of data every day, one of which is in the field of education. Data mining is a group of methods used to investigate and reveal complex relationships in very large data sets. Data here means information organized in a tabular format, as is often used in relational database management. This research uses data from the academic section of ITB Ahmad Dahlan, namely data on students of the Information Systems study program from 2019 to 2022. The attributes that will be used for this research are student gender, student employment status and student achievement index. Recommendations for promotional strategies to increase the number of new students are to conduct visits to high schools or vocational schools. Not only that, the new student admission team can also promote to companies or offices.

Application of K-means Clustering Algorithm in Rice Production of Tamil Nadu, India

Aim: (i) To Cluster the Rice data using K-Means clustering algorithm. (ii) To helps the study of crop yield prediction.
 Study Design: K-Means clustering technique is one of the most common exploratory data analysis used to get an intuition about the structure of the data.
 Place and Duration of Study: Time Series crop data were collected from the season and crop report, Directorate of Economics and Statistics, Chennai for the period of 2015-2020.
 Methodology: The machine learning algorithm of big data analytics method such as K-means clustering algorithm helps to predict the paddy yield accurately in Tamil Nadu. The performance of the technique is examined through the determinable value of k by Elbow method and Silhouette method which helps in the crop yield prediction.
 Results: The observed results show that there is a positive relationship between area, production, area under irrigation, minimum temperature, and relative humidity and a close negative relationship with moisture and wind speed. Additionally, two clusters were identified with cluster 2 having the highest mean value, followed by 1. The identification of the highest mean clusters will guide farmers on where best to concentrate on when planting their crops in ordered to improve productivity and crop yield.
 Conclusion: This study reveals a scalable, simple, and reduced method for correctly assessing rice production over a large area using publicly released multi-source data, which may have been used to calculate crop production in areas with rarely observed data and all around the world.

Application of K-Means Clustering Algorithm to Commercial Parameters of Pleurotus spp. Cultivated on Representative Agricultural Wastes from Province of Guayas.

Data of the commercial parameters of Pleurotus ostreatus and Pleurotus djamor were analyzed using the data mining technique: K-means clustering algorithm. The parameters evaluated were: biological efficiency, crop yield ratio, productivity rate, nutritional composition, antioxidant and antimicrobial activities in the production of fruit bodies of 50 strains of Pleurotus ostreatus and 50 strains of Pleurotus djamor, cultivated on the most representative agricultural wastes from the province of Guayas: 80% sugarcane bagasse and 20% wheat straw (M1), and 60% wheat straw and 40% sugarcane bagasse (M2). The database of the parameters obtained in experimental procedures was grouped into three clusters, providing a visualization of the strains with a higher relation to each parameter (vector) measured.

Application of K-Means Clustering Algorithm in Grouping Inventory Data at Putra Shop

Putra Mart store is one of the supermarkets in Bintuhan City., on Jl. South Kaur Cold Water. Putra shop is one of the shops engaged in supplying goods to small shops in Bengkulu City. The goods supply system at Toko Putra still uses a manual system in recording data, both data on availability of goods, data for store partners, and data on supply of goods to store partners. To help increase the supply of goods at Toko Putra, there needs to be an application that can determine what items should be in Toko Putra by looking at transaction data for supply of goods to store partners. Determination of stock of goods is done by grouping data on supply of goods through 2 groups, namely large groups and small groups. The application for grouping supply data at Toko Putra was created using the Visual Basic .Net programming language and SQL Server 2008 database by applying the K-Means Clustering Method. The grouping is done based on data on supply of goods per year obtained from Toko Putra. The application is able to analyze goods supply data by producing 2 clusters, namely Many and Few through the K-Means Clustering method approach. In addition, the results of this grouping can help the Putra Shop in managing inventory at the Putra Shop by looking at the results of the clustering that has been done. Based on the results of the tests that have been carried out, the application for grouping data on supply of goods at Toko Putra can provide information based on 2 groups, namely many and few. From the supply data in 2020, the results obtained are Cluster C1 as many as 4 and Cluster C2 as many as 13

Application of K-Means Clustering Algorithm in Online English Learning Effect Evaluation

Application of K-Means Clustering Algorithm in Online English Learning Effect Evaluation

Penerapan Algoritma K-Means Clustering untuk Pengelompokan Harga Eceran Beras di Pasar Tradisional Berdasarkan Wilayah Kota

Rice is the main food commodity of the Indonesian people, almost all residents in this country consume rice every day. This causes rice commodities to have a very strategic value, apart from being in control of the lives of many people, it can also be used as a parameter of the country's economic and social stability. This study discusses the application of K-Means Clustering Algorithm for Grouping Retail Prices of Rice in Traditional Markets. The source of this research data is collected based on documents describing the retail price group of rice produced by the National Statistics Agency. The data used in this study are data from 2011-2016 consisting of 33 cities. Data is processed by clustering in 3 clusters, namely high population level clusters, medium and low population level clusters. Centroid data for high population level clusters 10,776, Centroid data for moderate population level clusters 9,436 and Centroid data for low population clusters 8,590. To obtain an assessment based on the grouping of the average retail price of rice in traditional markets in 33 cities with high clusters (C1) of 11 cities namely Padang, Pekanbaru, Tanjung Pinang, Bandar Lampung, Jakarta, Pontianak, Palangkaraya, Banjarmasin, Ternate, Jayapura and Manokwari for medium cluster (C2) as many as 11 cities and for low cluster (C3) as many as 11 cities. From the results of this study can be used as input for the government, especially in cities, so that cities that are included in the high cluster can normalize the retail price of rice in each city area.

Application of K-Means Clustering Algorithm for Classification of NBA Guards

Application of K-Means Clustering Algorithm for Classification of NBA Guards

PENERAPAN ALGORITMA K-MEANS CLUSTERING PADA K-HARMONIC MEANS UNTUK SCHEDULE PREVENTIVE MAINTENANCE SERVICE

Vehicle maintenance is a very important sector in terms of economy and safety, a good understanding of vehicle maintenance is very important from the owner of the vehicle it self or from the company. Maintenance for the vehicle is considered as part structure of the activity in a series of improvements, as well as a planned activity to prevent potential errors resulting in damage. Schedule preventive maintenance is one of the methods that are used for vehicle maintenance scheduling. SPM is widely used because it can determine component reliability item, so as to reduce the cost of repairs, but this method has the disadvantage that reparations are made to the unit item could potentially breakdown, as well as the application of SPM only on certain types of vehicles. To solve this problem it is proposed the one application of a method, algorithm K-Means Clustering is one of the methods to be applied in the schedule vehicle maintenance services, K-Means algorithm is widely used because it is easy and simple. From the models created will then be tested using Confucion Matrix to determine how the level of accuracy, and describes the results of a positive predictive accuracy results are correct, the positive predictions were wrong, negative predictions are true, and false negative predictions. From these experiments showed that the application of K-Means Clustering algorithms in the vehicle's maintenance schedule capable of generating predictive value and accuracy that is optimal by 70%.