Efficiency of hierarchic agglomerative clustering using the ICL distributed array processor
Journal of Documentation
A parallel algorithm for computing minimum spanning trees
SPAA '92 Proceedings of the fourth annual ACM symposium on Parallel algorithms and architectures
Parallel algorithms for hierarchical clustering
Parallel Computing
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Clustering in massive data sets
Handbook of massive data sets
Accurate Prediction of Orthologous Gene Groups in Microbes
CSB '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference
A fast, parallel spanning tree algorithm for symmetric multiprocessors (SMPs)
Journal of Parallel and Distributed Computing
An OpenMP algorithm and implementation for clustering biological graphs
Proceedings of the first workshop on Irregular applications: architectures and algorithm
DICLENS: Divisive Clustering Ensemble with Automatic Cluster Number
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
The Impact of Normalization and Phylogenetic Information on Estimating the Distance for Metagenomes
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Objective function-based clustering
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
A framework for Multi-Agent Based Clustering
Autonomous Agents and Multi-Agent Systems
p-PIC: Parallel power iteration clustering for big data
Journal of Parallel and Distributed Computing
An evolutionary computational model applied to cluster analysis of DNA microarray data
Expert Systems with Applications: An International Journal
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Large sets of bioinformatical data provide a challenge in time consumption while solving the cluster identification problem, and that is why a parallel algorithm is so needed for identifying dense clusters in a noisy background. Our algorithm works on a graph representation of the data set to be analyzed. It identifies clusters through the identification of densely intraconnected subgraphs. We have employed a minimum spanning tree (MST) representation of the graph and solve the cluster identification problem using this representation. The computational bottleneck of our algorithm is the construction of an MST of a graph, for which a parallel algorithm is employed. Our high-level strategy for the parallel MST construction algorithm is to first partition the graph, then construct MSTs for the partitioned subgraphs and auxiliary bipartite graphs based on the subgraphs, and finally merge these MSTs to derive an MST of the original graph. The computational results indicate that when running on 150 CPUs, our algorithm can solve a cluster identification problem on a data set with 1,000,000 data points almost 100 times faster than on single CPU, indicating that this program is capable of handling very large data clustering problems in an efficient manner. We have implemented the clustering algorithm as the software CLUMP.