Alternatives to the k-means algorithm that find better clusterings
These approaches are beneficial but they are attempting to fix the problems of clustering algorithms externally
Spectral Rotation versus K-Means in Spectral Vectors Clustering
Spectral clustering has been a popular data clustering algorithm. This category of approaches often resort to other clustering methods such as K-Means
Performance Comparison of Incremental K-means and Incremental
improved version of K-means clustering algorithm. This algorithm will perform better than DBSCAN while handling clusters of circularly distributed data
A Better k-means++ Algorithm via Local Search
The k-means++ seeding algorithm (Arthur & Vassilvitskii less than 1.0013
Improving the K-means algorithm using improved downhill simplex
that the proposed algorithm is clusters the data better than k- means algorithm because the improved downhill simplex algorithm selects the better fist
K-means vs Mini Batch K-means: A comparison
The advantage of this algorithm is to reduce the computational cost by not using all the dataset each iteration but a subsample of a fixed size. This strategy
K-NN Classifier Performs Better Than K-Means Clustering in Missing
Thus the purpose of K-mean clustering is to classify the data. V. Imputation with K-nearest Neighbor Algorithm. Nearest Neighbour Analysis is a method for
Analysis of K-Means and K-Medoidss Performance Using Big Data
time and time complexity of the algorithm. In terms of accuracy K-Medoids is better than K-Means with an average accuracy of 63.24%
A Comparison of Document Clustering Techniques
standard K-means approach and as good or better than the hierarchical approaches that we However if one clustering algorithm performs better than other ...
EXPERIMENTS ON HYPOTHESIS FUZZY K-MEANS IS BETTER
clustering algorithm. In this paper we prove the hypothesis “Fuzzy K-Means is better than K-Means for. Clustering” through both literature and empirical
