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Unsupervised K-Means Clustering Algorithm

IEEE Access, 2020
The k-means algorithm is generally the most known and used clustering method. There are various extensions of k-means to be proposed in the literature. Although it is an unsupervised learning to clustering in pattern recognition and machine learning, the
Kristina P. Sinaga, Miin-Shen Yang
doaj   +4 more sources

Causal K-Means Clustering. [PDF]

J R Stat Soc Series B Stat Methodol
Abstract Causal effects are often characterized at the population level, which can mask important heterogeneity across latent subgroups. Since the subgroup structure is unknown, identifying and evaluating subgroup specific effects is substantially more challenging than standard population level analysis.
Kim K, Kim J, Kennedy EH.
europepmc   +3 more sources

Metode Elbow dalam Optimasi Jumlah Cluster pada K-Means Clustering

Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer, 2023
K-Means clustering merupakan salah satu strategi yang digunakan dalam analisis data dan machine learning untuk mengelompokkan data menjadi beberapa kelompok (cluster) berdasarkan kemiripan fitur atau atributnya.
Nadia Annisa Maori, Evanita Evanita
doaj   +2 more sources

K-Means Cloning: Adaptive Spherical K-Means Clustering [PDF]

Algorithms, 2018
We propose a novel method for adaptive K-means clustering. The proposed method overcomes the problems of the traditional K-means algorithm. Specifically, the proposed method does not require prior knowledge of the number of clusters.
Abdel-Rahman Hedar, Abdel-Monem M. Ibrahim, Alaa E. Abdel-Hakim, Adel A. Sewisy   +3 more
doaj   +3 more sources

Spherical k-Means Clustering

Journal of Statistical Software, 2012
Clustering text documents is a fundamental task in modern data analysis, requiring approaches which perform well both in terms of solution quality and computational efficiency.
Kurt Hornik, Ingo Feinerer, Martin Kober, Christian Buchta   +3 more
doaj   +3 more sources

Unsupervised Multi-View K-Means Clustering Algorithm

IEEE Access, 2023
Since advanced technologies via social media, internet, virtual communities and networks and internet of things (IoT), there are more multi-view data to be collected.
Miin-Shen Yang, Ishtiaq Hussain
doaj   +2 more sources

k-Means+++: Outliers-Resistant Clustering

Algorithms, 2020
The k-means problem is to compute a set of k centers (points) that minimizes the sum of squared distances to a given set of n points in a metric space. Arguably, the most common algorithm to solve it is k-means++ which is easy to implement and provides a
Adiel Statman, Liat Rozenberg, Dan Feldman   +2 more
doaj   +2 more sources

The impact of neglecting feature scaling in k-means clustering. [PDF]

PLoS One
Despite the popularity of k-means clustering, feature scaling before applying it can be an essential yet often neglected step. In this study, feature scaling via five methods: Z-score, Min-Max normalization, Percentile transformation, Maximum absolute ...
Wongoutong C.
europepmc   +2 more sources

Research on K-Value Selection Method of K-Means Clustering Algorithm

J, 2019
Among many clustering algorithms, the K-means clustering algorithm is widely used because of its simple algorithm and fast convergence. However, the K-value of clustering needs to be given in advance and the choice of K-value directly affect the ...
Chunhui Yuan, Haitao Yang
exaly   +2 more sources

Image Segmentation Using K -means Clustering Algorithm and Subtractive Clustering Algorithm

Procedia Computer Science, 2015
Image segmentation is the classification of an image into different groups. Many researches have been done in the area of image segmentation using clustering. There are different methods and one of the most popular methods is k-means clustering algorithm.
Nameirakpam Dhanachandra, Yambem Jina Chanu   +1 more
exaly   +2 more sources