[PDF][PDF] New fast k-means clustering algorithm using modified centroid selection method
S Sujatha, AS Sona - … of Engineering Research & Technology (IJERT …, 2013 - academia.edu
Cluster analysis is a major technique for classifying a „mountain‟ of information into
manageable meaningful piles. It is a data reduction tool that creates subgroups that are …
manageable meaningful piles. It is a data reduction tool that creates subgroups that are …
Selection of K in K-means clustering
The K-means algorithm is a popular data-clustering algorithm. However, one of its
drawbacks is the requirement for the number of clusters, K, to be specified before the …
drawbacks is the requirement for the number of clusters, K, to be specified before the …
An improved parameter less data clustering technique based on maximum distance of data and lioyd k-means algorithm
K-means algorithm is very well-known in large data sets of clustering. This algorithm is
popular and more widely used for its easy implementation and fast working. However, it is …
popular and more widely used for its easy implementation and fast working. However, it is …
Improved k-means clustering based on genetic algorithm
W Min, Y Siqing - 2010 International Conference on Computer …, 2010 - ieeexplore.ieee.org
The K-means algorithm is widely used because of its reliable theory, simple algorithm, fast
convergence and it can effectively handle large data sets. However, the traditional K-means …
convergence and it can effectively handle large data sets. However, the traditional K-means …
[PDF][PDF] K-Means clustering optimization using the elbow method and early centroid determination based-on mean and median
E Umargono, JE Suseno, S Gunawan - Proceedings of the …, 2019 - scitepress.org
The most widely used algorithm in the cluster partitioning method is the K-Means algorithm.
Historically K-Means is still the best grouping algorithm among other grouping algorithms …
Historically K-Means is still the best grouping algorithm among other grouping algorithms …
The uniform effect of k-means clustering
J Wu, J Wu - Advances in K-means Clustering: A Data Mining …, 2012 - Springer
This chapter studies the uniform effect of K-means clustering. As a well-known and widely
used partitional clustering method, K-means has attracted great research interests for a very …
used partitional clustering method, K-means has attracted great research interests for a very …
[PDF][PDF] K-means for spherical clusters with large variance in sizes
Data clustering is an important data exploration technique with many applications in data
mining. The k-means algorithm is well known for its efficiency in clustering large data sets …
mining. The k-means algorithm is well known for its efficiency in clustering large data sets …
An improved K-Means clustering algorithm
J Wang, X Su - 2011 IEEE 3rd international conference on …, 2011 - ieeexplore.ieee.org
The K-Means clustering algorithm is proposed by Mac Queen in 1967 which is a partition-
based cluster analysis method. It is used widely in cluster analysis for that the K-means …
based cluster analysis method. It is used widely in cluster analysis for that the K-means …