Results 271 to 280 of about 15,644,966 (296)

Elkan’s k-Means Algorithm for Graphs

2010
This paper proposes a fast k-means algorithm for graphs based on Elkan's k-means for vectors. To accelerate the k-means algorithm for graphs without trading computational time against solution quality, we avoid unnecessary graph distance calculations by exploiting the triangle inequality of the underlying distance metric.
Brijnesh J. Jain, Klaus Obermayer
openaire   +1 more source

An Enhancement of K-means Clustering Algorithm

2009 International Conference on Business Intelligence and Financial Engineering, 2009
Abstract—K-means clustering algorithm and one of its Enhancements are studied in this paper. Clustering is the classification of objects into different groups, or more precisely, the partitioning of a data set into subsets (clusters), so that the data in each subset (ideally) share some common trait - often proximity according to some defined distance ...
Jirong Gu, Jieming Zhou, Xianwei Chen
openaire   +2 more sources

A note on constrained k-means algorithms

Pattern Recognition, 2000
Abstract This paper describes extensions to the k -means algorithm for clustering data sets. By adding suitable constraints into the mathematical program formulation, an approach is developed, which allows the use of the k -means paradigm to efficiently cluster data sets with the fixed number of objects in each cluster.
openaire   +1 more source

The MinMax k-Means clustering algorithm

Pattern Recognition, 2014
Applying k-Means to minimize the sum of the intra-cluster variances is the most popular clustering approach. However, after a bad initialization, poor local optima can be easily obtained. To tackle the initialization problem of k-Means, we propose the MinMax k-Means algorithm, a method that assigns weights to the clusters relative to their variance and
Grigorios Tzortzis, Aristidis Likas
openaire   +2 more sources

A Kernel Iterative K-Means Algorithm

2019
In this paper Mercer kernels with certain invariance properties are briefly introduced and an apparently not well-known construction using certain cohomology groups is described. As a consequence some kernels arising from this are given. Hence a kernel version of an iterative k-means algorithm due to Duda et al. is exhibited.
openaire   +1 more source

Weight in Competitive K-Means Algorithm

2012
K-means algorithm is a well-known clustering method. Typically, the k-means algorithm treats all features fairly and sets weights of all features equally when evaluating dissimilarity. However, experiment results show that a meaningful clustering phenomenon often occurs in a subspace defined by some specific features.
Tingting Cui, XinBin Zhao, Zhe Wang 0017, Yinqian Zhang   +3 more
openaire   +1 more source

A conceptual version of the K-means algorithm

Pattern Recognition Letters, 1995
Clustering techniques are important for knowledge acquisition. Traditionally, numerical clustering methods have been viewed in opposition to conceptual clustering methods developed in Artificial Intelligence. Numerical techniques emphasize the determination of homogeneous clusters but provide low-level descriptions of clusters. A conceptual approach is
openaire   +1 more source

K-Means Clustering Algorithms: A Comprehensive Review, Variants Analysis, and Advances in the Era of Big Data

Information Sciences, 2023
Laith Abualigah, Absalom E Ezugwu, Abiodun M Ikotun   +2 more
exaly  

A Unified Form of Fuzzy C-Means and K-Means Algorithms and Its Partitional Implementation

Knowledge-Based Systems, 2021
Daniel Iercan, Radu-Emil Precup, Alexandra-Bianca Borlea   +2 more
exaly  

An Effective and Adaptable K-Means Algorithm for Big Data Cluster Analysis

Pattern Recognition, 2023
Haize Hu, Jianxun Liu
exaly