k-means

Clustering analysis has been used in many field of life. K-Means cluster is classic partitioning Clustering.

The action potentials' features are extracted by PCA, the action potential classification is implemented by the improved K-means algorithm.

LEACH routing protocol for the lack of a k-means clustering based on the multi-hop routing algorithm clustering LEACH-KMCM.

A wide range of existing K-means clustering method to select the initial cluster centers have their own advantages and disadvantages.

CONCLUSIONS: K-means objectively separates contractile and non-contractile tissue components.

Common approaches to unsupervised learning include k-Means, hierarchical clustering, and self-organizing maps.

First we make a loose classification with k-means clustering algorithm to fix a category of interest.

The result shows that k-means cluster isn't suitable for indicators' classification and factor analysis is a good classification method.

We use pairwise constraints k-means algorithm to cluster in the subset in which the closures are changed by closures center.

The experiments indicate that the rough K-means based on self-adaptive weights is an effective rough clustering algorithm.

In this dissertation, K-means algorithm is modified, employing spatial context information.

Second, the initial segmented regions were obtained using K-means clustering algorithm in the feature space.

After that, unsupervised K-means clustering was calculated to complete spike sorting.

Popular approaches include k-Means and hierarchical clustering.

This paper proposes a method based on Principal Component Analysis(PCA) combined with improved K-means for action potential classification.

This directory contains code implementing the K-means algorithm.

After analyzing the traditional clustering algorithms, the paper presents a new clustering ensemble method based on K-means to cluster data.

A recognition method based on HMM and K-means cluster is proposed through extracting LPC characteristic from acoustic target.

I use mathematical statistics analysis, such as confidence intervals, hypothesis testing, K-means Cluster, regression analysis and so on.

The Research of a Hybrid Clustering Algorithm Incorporating PSO into k- Means Algorithm

Application of A Modified Segmental K-Means Training Algorithm to Keyword Spotting

Application of Modified K-means clustering algorithm to Lithofacies Identification

Variant of K-means algorithm for document clustering: optimization initial centers

New validity index for determining the number of clusters in K-means clustering

first , the whole system was decomposed into several subsystems by adopting frizzy k - means cluster

Road Recognition Algorithm Using Principal Component Neural Networks and K-Means

K-means clustering algorithm based distance concentration

Traffic Pattern Identification of Elevator Group Control System Based on Immune Evolutionary K-means Clustering Algorithm

Clustering random samples of multi-gaussian distribution with k-means

Research on Extracting the Sclerenchyma Region in a Eucalyptus Image Based on K-means Clustering

Optimization of K-means Clustering by Feature Weight Learning

Application of Rough Set and K-means clustering in image segmentation

Incremental K-means Method Based on Initialisation of Cluster Centers and Its Application in News Event Detection

Novel clustering algorithm using K-means Chernoff faces

Genetic k-Means Algorithm for Clustering of Large-Scale Vector Space

An Effective Distributed k-Means Clustering Algorithm Based on the Pretreatment of Vectors'Inner-Product

An Advanced Algorithm for K-Means Document Clustering

Principal Component Analysis Based on Shock Wave from Supersonic Projectiles and K-means Algorithm

An Iterated Local Search Algorithm for K-Means Clustering

A New Approach to Ascertain the Initial Clustering Centers of K-means

Efficient Data Stream Clustering Algorithm Based on k-Means Partitioning and Density

An Efficient Data Stream Outliers Detection Algorithm Based on k-Means Partitioning

A Reinforcement Learning Method Based on Node-Growing k-Means Clustering Algorithm

On the K-means Clustering of the Gene Expression Data Based on Genetic Algorithm

Fuzzy K-means algorithms based on membership function improvement

Reinforcement Learning Method of Continuous State Adaptively Discretized Based on K-means Clustering

Design of fuzzy K-means-based fuzzy classifier

Image Segmentation Based on Rough Entropy and K-Means Clustering Algorithm

An Improved K-Means Clustering Algorithm Based on Feature Weighting

Improved Digital Image Watermarking Based on K-Means Algorithm

Fuzzy K-means clustering algorithm to non-spherical shape data based on kernel

New Image Segmentation for Robot-Soccer Based on K-means Clustering

Research of K-Means Algorithm in Disk

K-means Clustering Algorithm Based on Self-Adoptively Selected Density Radius

Fault Knowledge Ming Based on Rough Set and K-Means Clustering

K-Means Clustering Based on Genetic Algorithm

A Method of Image Segmentation Based on K-means Algorithm