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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

Semantic Scholar uses AI to extract papers important to this topic.
2019
2019
TPM: A GPS-based Trajectory Pattern Mining System
With the development of big data and artificial intelligence, the technology of urban computing becomes more mature and widely… 
2019
2019
Image Splicing Localization Using Superpixel Segmentation and Noise Level Estimation
With the development of computer and artificial intelligence technology, the authenticity of digital images has been seriously… 
2018
2018
Mixture of Convolutional Neural Networks for Image Classification
  • 2018
  • Corpus ID: 52905887
Two of the main issues that need to be considered when dealing with Mixture of Experts (ME) are how to partition the training… 
2017
2017
Extracting Top Trends from Twitter Discussions in Bulgarian
Social networks offer plenty opportunities and areas for scientific research to dabble in user opinion mining and text analysis… 
2017
2017
A novel training sample selection approach for near-infrared spectroscopy model and its industrial application
Near-infrared (NIR) spectroscopy has been widely applied for the real-time measurements of quality variables, which plays an… 
2016
2016
Denoising with Patch-based Principal Component Analysis
One important task in image processing is noise reduction, which requires to recover image information by removing noise without… 
2016
2016
Problem of identifying destructive informational influence in social networks
The article deals with the task of identifying the elements of the information influence in messages of social networks… 
2014
2014
Further heuristics for $k$-means: The merge-and-split heuristic and the $(k, l)$-means
Finding the optimal $k$-means clustering is NP-hard in general and many heuristics have been designed for minimizing… 
2014
2014
Notes on using Determinantal Point Processes for Clustering with Applications to Text Clustering
In this paper, we compare three initialization schemes for the KMEANS clustering algorithm: 1) random initialization (KMEANSRAND… 
2010
2010
RBL Global Toolbar with Clustering Algorithm for Fake Website Detection
Phishing is a current social engineering attack that results in online identity theft. Phishing web pages generally use similar… 
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