Results 311 to 320 of about 940,524 (345)

Kidney disease detection and segmentation using artificial neural network and multi-kernel k-means clustering for ultrasound images

, 2020
The main aim of this paper is to design and develop an approach for kidney disease detection and segmentation using a combination of clustering and classification approach.
A. Nithya, Ahilan Appathurai, N. Venkatadri, D. Ramji, C. A. Palagan   +4 more
semanticscholar   +1 more source

Discriminative K-means for Clustering.

2008
We present a theoretical study on the discriminative clustering framework, recently proposed for simultaneous subspace selection via linear discriminant analysis (LDA) and clustering. Empirical results have shown its favorable performance in comparison with several other popular clustering algorithms. However, the inherent relationship between subspace
Ye, J., Zhao, Z., Wu, M.
openaire   +2 more sources

Random Projection for k-means Clustering

2018
We study how much the k-means can be improved if initialized by random projections. The first variant takes two random data points and projects the points to the axis defined by these two points. The second one uses furthest point heuristic for the second point.
Fränti Pasi, Sieranoja Sami
openaire   +1 more source

k*-Means — A Generalized k-Means Clustering Algorithm with Unknown Cluster Number

2002
This paper presents a new clustering technique named STepwise Automatic Rival-penalized (STAR) k-means algorithm (denoted as k*-means), which is actually a generalized version of the conventional k-means (MacQueen 1967). Not only is this new algorithm applicable to ellipse-shaped data clusters rather than just to ball-shaped ones like the k-means ...
openaire   +1 more source

Clustering by k-means

2009
Clustering techniques are used for finding suitable groupings of samples belonging to a given set of data. There is no knowledge a priori about these data. Therefore, such set of samples cannot be considered as a training set, and classification techniques cannot be used in this case.
Antonio Mucherino, Petraq J. Papajorgji, Panos M. Pardalos   +2 more
openaire   +1 more source

A k-means clustering algorithm

, 1979
J. Hartigan, M. A. Wong
semanticscholar   +1 more source

Data clustering: 50 years beyond K-means

Pattern Recognition Letters, 2008
Anil K. Jain
semanticscholar   +1 more source

K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data

Information Sciences, 2023
Absalom E Ezugwu, Laith Abualigah, Belal Abuhaija   +2 more
exaly  

k-means clustering and kNN classification based on negative databases

Applied Soft Computing, 2021
Dongdong Zhao, Xiaoyi Hu, Shengwu Xiong, Jing Tian, Jianwen Xiang, Jing Zhou, Huanhuan Li   +6 more
semanticscholar   +1 more source

K-means clustering via principal component analysis

International Conference on Machine Learning, 2004
C. Ding, Xiaofeng He
semanticscholar   +1 more source