Results 31 to 40 of about 940,524 (345)

Improved Soft-k-Means Clustering Algorithm for Balancing Energy Consumption in Wireless Sensor Networks [PDF]

IEEE Internet of Things Journal, 2021
Energy load balancing is an essential issue in designing wireless sensor networks (WSNs). Clustering techniques are utilized as energy-efficient methods to balance the network energy and prolong its lifetime. In this article, we propose an improved soft-
Botao Zhu, E. Bedeer, Ha H. Nguyen, Robert Barton, Jérôme Henry   +4 more
semanticscholar   +1 more source

Metode Elbow dan K-Means Guna Mengukur Kesiapan Siswa SMK Dalam Ujian Nasional

Jurnal Teknologi dan Sistem Informasi, 2020
Keberhasilan siswa dalam menempuh ujian nasional (UN) dapat terlihat dari perolehan nilai mata pelajaran yang diujikan, tiga diantaranya adalah nilai matematika, Bahasa Indonesia, dan Bahasa Inggris.
Ninik Tri Hartanti
doaj   +1 more source

Discriminative k-means clustering [PDF]

The 2013 International Joint Conference on Neural Networks (IJCNN), 2013
The k-means algorithm is a partitional clustering method. Over 60 years old, it has been successfully used for a variety of problems. The popularity of k-means is in large part a consequence of its simplicity and efficiency. In this paper we are inspired by these appealing properties of k-means in the development of a clustering algorithm which accepts
openaire   +2 more sources

Global optimality in k -means clustering [PDF]

Information Sciences, 2018
We study the problem of finding an optimum clustering, a problem known to be NP-hard. Existing literature contains algorithms running in time proportional to the number of points raised to a power that depends on the dimensionality and on the number of clusters.
Cristina Tîrnauca, Domingo Gómez-Pérez, José L. Balcázar, José L. Montaña   +3 more
openaire   +4 more sources

Corn Leaf Diseases Diagnosis Based on K-Means Clustering and Deep Learning

IEEE Access, 2021
Accurate diagnosis of corn crop diseases is a complex challenge faced by farmers during the growth and production stages of corn. In order to address this problem, this paper proposes a method based on K-means clustering and an improved deep learning ...
Helong Yu, Jiawen Liu, Chengcheng Chen, Ali Asghar Heidari, Qian Zhang, Huiling Chen, Majdi M. Mafarja, H. Turabieh   +7 more
semanticscholar   +1 more source

Subspace K-means clustering

Behavior Research Methods, 2013
To achieve an insightful clustering of multivariate data, we propose subspace K-means. Its central idea is to model the centroids and cluster residuals in reduced spaces, which allows for dealing with a wide range of cluster types and yields rich interpretations of the clusters. We review the existing related clustering methods, including deterministic,
Timmerman, Marieke E., Ceulemans, Eva, De Roover, Kim, Van Leeuwen, Karla   +3 more
openaire   +2 more sources

Differentially Private K-Means Clustering [PDF]

Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy, 2016
There are two broad approaches for differentially private data analysis. The interactive approach aims at developing customized differentially private algorithms for various data mining tasks. The non-interactive approach aims at developing differentially private algorithms that can output a synopsis of the input dataset, which can then be used to ...
Dong Su, Jianneng Cao, Ninghui Li 0001, Elisa Bertino, Hongxia Jin   +4 more
openaire   +2 more sources

A survey of kernel and spectral methods for clustering [PDF]

, 2007
Clustering algorithms are a useful tool to explore data structures and have been employed in many disciplines. The focus of this paper is the partitioning clustering problem with a special interest in two recent approaches: kernel and spectral methods ...
Masulli, F., CAMASTRA, Francesco, Francesco Camastra, Filippone, M., Maurizio Filippone, FILIPPONE M, Francesco Masulli, Camastra, F., ROVETTA S., MASULLI F, Stefano Rovetta, Rovetta, S.   +11 more
core   +1 more source

The application of K-means clustering for province clustering in Indonesia of the risk of the COVID-19 pandemic based on COVID-19 data

Quality & Quantity: International Journal of Methodology, 2021
This study was conducted with the aim to the clustering of provinces in Indonesia of the risk of the COVID-19 pandemic based on coronavirus disease 2019 (COVID-19) data.
Dahlan Abdullah, S. Susilo, A. S. Ahmar, R. Rusli, R. Hidayat   +4 more
semanticscholar   +1 more source

An Overlapping Subspace K-Means Clustering Algorithm [PDF]

Jisuanji gongcheng, 2020
Most of existing clustering algorithms for high-dimensional sparse data do not consider overlapping class clusters and outliers,resulting in unsatisfactory clustering results.Therefore,this paper proposes an overlapping subspace K-Means clustering ...
LIU Yuhang, MA Huifang, LIU Haijiao, YU Li
doaj   +1 more source