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Global k-means++: an effective relaxation of the global k-means clustering algorithm
AbstractThe k -means algorithm is a prevalent clustering method due to its simplicity, effectiveness, and speed. However, its main disadvantage is...
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Selecting optimal k for K-means in image segmentation using GLCM
Region growing, clustering, and thresholding are some of the segmentation techniques that are employed on images. K-means clustering is one of the...
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Questions clustering using canopy-K-means and hierarchical-K-means clustering
In questions datasets, several questions could produce duplicates since they are similar questions due to the ability to write a question in...
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k-Means-MIND: comparing seeds without repeated k-means runs
A key drawback of the popular k -means clustering algorithm is its susceptibility to local minima. This problem is often addressed by performing...
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K-means Based Transfer Learning Algorithm
Focused on the issue that most transfer learning methods ignore the intra-domain distribution structures of the target domain, an algorithm based on...
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Coordinate Descent for k-Means with Differential Privacy
In recent years, Lloyd’s heuristic has become one of the most useful methods to solve k-means problem due to its simplicity. However, Lloyd’s...
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Performance of the K-means and fuzzy C-means algorithms in big data analytics
Nowadays, cloud computing is used by most organizations to utilize cloud resources and services in dealing with big data. Besides, machine learning...
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K-means and meta-heuristic algorithms for intrusion detection systems
In this research paper, we propose a two-stage hybrid approach that uses machine learning techniques and meta-heuristic algorithms. The first step,...
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Impact of new seed and performance criteria in proposed rough k-means clustering
Rough k-means algorithm is one of the widely used soft clustering methods for clustering. However, the rough k-means clustering algorithm has certain...
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PCMeans: community detection using local PageRank, clustering, and K-means
With the rise of social networks, the task of community detection in networks has become increasingly difficult in recent years. In this study, we...
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K-Means Clustering
Please download the sample Excel files from . Double-click Chapter3-1a.xlsx to open it.
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Determination of Optimum K Value for K-means Segmentation of Diseased Tea Leaf Images
Detecting diseases from the leaf images of a plant is an important and challenging task. Various image processing techniques like pre-processing,...
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Accelerating k-Means Clustering with Cover Trees
The k-means clustering algorithm is a popular algorithm that partitions data into k clusters. There are many improvements to accelerate the standard...
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Greedy centroid initialization for federated K-means
We study learning from unlabeled data distributed across clients in a federated fashion where raw data do not leave the corresponding devices. We...
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Improved Genetic Algorithm Based k-means Cluster for Optimized Clustering
The Human Freedom Index (HFI) is an annual evaluation that measures a variety of factors, such as the rule of law, security, religion, expression,...
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Sub-One Quasi-Norm-Based k-Means Clustering Algorithm and Analyses
Recognizing the pivotal role of choosing an appropriate distance metric in designing the clustering algorithm, our focus is on innovating the k -means...
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Opinion Mining Using Optimized K-Means Algorithm and a Word Weighting Technique
Twitter is one of the commonly used social networking sites in which users can post their opinions as tweets. These tweets can be analyzed by...
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A New Theoretical Framework for K-Means-Type Clustering
One of the fundamental clustering problems is to assign n points into k clusters based on the minimal sum-of-squares(MSSC), which is known to be...
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k-Median/Means with Outliers Revisited: A Simple Fpt Approximation
We revisit the classical metric k-median/means with outliers in this paper, whose proposal dates back to (Charikar, Khuller, Mount, and Narasimhan...
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Nonparametric K-means clustering-based adaptive unsupervised colour image segmentation
Image segmentation focuses at highlighting region of interest within the image, by accumulation of pixels based on given properties. This task...
