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K-means++

Known as: Kmeans++ 
In data mining, k-means++ is an algorithm for choosing the initial values (or "seeds") for the k-means clustering algorithm. It was proposed in 2007… 
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Related topics

Related topics

11 relations
Cluster analysisData miningEnvironment for DeveLoping KDD-Applications Supported by Index-StructuresImage segmentation

Papers overview

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Highly Cited
2019
Highly Cited
2019
Fair Coresets and Streaming Algorithms for Fair k-means
We study fair clustering problems as proposed by Chierichetti et al. [CKLV17]. Here, points have a sensitive attribute and all… 
2019
2019
Evaluation of modified adaptive k-means segmentation algorithm
Segmentation is the act of partitioning an image into different regions by creating boundaries between regions. k-means image… 
Highly Cited
2017
Highly Cited
2017
Adaptive density peak clustering based on K-nearest neighbors with aggregating strategy
Highly Cited
2016
Highly Cited
2016
Fast and Provably Good Seedings for k-Means
Seeding - the task of finding initial cluster centers - is critical in obtaining high-quality clusterings for k-Means. However, k… 
2016
2016
Semi-supervised k-means++
ABSTRACT Traditionally, practitioners initialize the k-means algorithm with centres chosen uniformly at random. Randomized… 
2016
2016
Dictionary learning for VQ feature extraction in ECG beats classification
2015
2015
Exploring Non-Homogeneity and Dynamicity of High Scale Cloud through Hive and Pig
Cloud environment is usually associated with non-homogeneity and dynamicity in terms of resource usage and access at all levels… 
Highly Cited
2015
Highly Cited
2015
Fuzzy C-means++: Fuzzy C-means with effective seeding initialization
Highly Cited
2014
Highly Cited
2014
An Algorithm for Online K-Means Clustering
This paper shows that one can be competitive with the k-means objective while operating online. In this model, the algorithm… 
Highly Cited
2013
Highly Cited
2013
Competitive K-Means, a New Accurate and Distributed K-Means Algorithm for Large Datasets
The tremendous growth in data volumes has created a need for new tools and algorithms to quickly analyze large datasets. Cluster… 
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