A simulated annealing algorithm for the clustering problem
Pattern Recognition
Cluster analysis and mathematical programming
Mathematical Programming: Series A and B - Special issue: papers from ismp97, the 16th international symposium on mathematical programming, Lausanne EPFL
ACM Computing Surveys (CSUR)
An Interior Point Algorithm for Minimum Sum-of-Squares Clustering
SIAM Journal on Scientific Computing
Variable Neighborhood Decomposition Search
Journal of Heuristics
Techniques for clustering gene expression data
Computers in Biology and Medicine
A Fast Approximation Algorithm for the k Partition-Distance Problem
ICCSA '09 Proceedings of the International Conference on Computational Science and Its Applications: Part II
Fast modified global k-means algorithm for incremental cluster construction
Pattern Recognition
A new method for GPU based irregular reductions and its application to k-means clustering
Proceedings of the Fourth Workshop on General Purpose Processing on Graphics Processing Units
Improved Parameterless K-Means: Auto-Generation Centroids and Distance Data Point Clusters
International Journal of Information Retrieval Research
Applying clustering and ensemble clustering approaches to phishing profiling
AusDM '09 Proceedings of the Eighth Australasian Data Mining Conference - Volume 101
Hi-index | 0.00 |
Clustering in gene expression data sets is a challenging problem. Different algorithms for clustering of genes have been proposed. However due to the large number of genes only a few algorithms can be applied for the clustering of samples. k-means algorithm and its different variations are among those algorithms. But these algorithms in general can converge only to local minima and these local minima are significantly different from global solutions as the number of clusters increases. Over the last several years different approaches have been proposed to improve global search properties of k-means algorithm and its performance on large data sets. One of them is the global k-means algorithm. In this paper we develop a new version of the global k-means algorithm: the modified global k-means algorithm which is effective for solving clustering problems in gene expression data sets. We present preliminary computational results using gene expression data sets which demonstrate that the modified k-means algorithm improves and sometimes significantly results by k-means and global k-means algorithms.