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A Clustering Algorithm based on Graph Connectivity
 Information Processing Letters
, 1999
"... We have developed a novel algorithm for cluster analysis that is based on graph theoretic techniques. ..."
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Cited by 99 (3 self)
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We have developed a novel algorithm for cluster analysis that is based on graph theoretic techniques.
An Algorithm for Clustering cDNAs for Gene Expression Analysis
 In RECOMB99: Proceedings of the Third Annual International Conference on Computational Molecular Biology
, 1999
"... We have developed a novel algorithm for cluster analysis that is based on graph theoretic techniques. A similarity graph is defined and clusters in that graph correspond to highly connected subgraphs. A polynomial algorithm to compute them efficiently is presented. Our algorithm produces a clusterin ..."
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Cited by 45 (4 self)
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We have developed a novel algorithm for cluster analysis that is based on graph theoretic techniques. A similarity graph is defined and clusters in that graph correspond to highly connected subgraphs. A polynomial algorithm to compute them efficiently is presented. Our algorithm produces a clustering with some provably good properties. The application that motivated this study was gene expression analysis, where a collection of cDNAs must be clustered based on their oligonucleotide fingerprints. The algorithm has been tested intensively on simulated libraries and was shown to outperform extant methods. It demonstrated robustness to high noise levels. In a blind test on real cDNA fingerprint data the algorithm obtained very good results. Utilizing the results of the algorithm would have saved over 70% of the cDNA sequencing cost on that data set. 1 Introduction Cluster analysis seeks grouping of data elements into subsets, so that elements in the same subset are in some sense more cl...
LEGClust  A Clustering Algorithm Based on Layered Entropic Subgraphs
, 2008
"... Hierarchical clustering is a stepwise clustering method usually based on proximity measures between objects or sets of objects from a given data set. The most common proximity measures are distance measures. The derived proximity matrices can be used to build graphs, which provide the basic structu ..."
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Cited by 4 (1 self)
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Hierarchical clustering is a stepwise clustering method usually based on proximity measures between objects or sets of objects from a given data set. The most common proximity measures are distance measures. The derived proximity matrices can be used to build graphs, which provide the basic structure for some clustering methods. We present here a new proximity matrix based on an entropic measure and also a clustering algorithm (LEGClust) that builds layers of subgraphs based on this matrix and uses them and a hierarchical agglomerative clustering technique to form the clusters. Our approach capitalizes on both a graph structure and a hierarchical construction. Moreover, by using entropy as a proximity measure, we are able, with no assumption about the cluster shapes, to capture the local structure of the data, forcing the clustering method to reflect this structure. We present several experiments on artificial and real data sets that provide evidence on the superior performance of this new algorithm when compared with competing ones.
Evaluating Graph Theoretic Clustering Algorithms For Reliable Multicasting
 Proceedings of IEEE GLOBECOM
, 2001
"... In reliable multicast protocols, each data packet being sent must be acknowledged. Collecting the acknowledgments centrally at the sources can cause ACKimplosion and can result in poor scalability. To overcome this, clustering algorithms which use virtual structures to gather acknowledgments were p ..."
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Cited by 3 (0 self)
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In reliable multicast protocols, each data packet being sent must be acknowledged. Collecting the acknowledgments centrally at the sources can cause ACKimplosion and can result in poor scalability. To overcome this, clustering algorithms which use virtual structures to gather acknowledgments were proposed. In this work, we analyze the complexities of three such clustering algorithms: Lorax, kdegree, and Selfadjust. We compare the quality of the virtual structures produced by these algorithms, focusing on the number of clusters, cluster size, cluster radius, and the optimal positioning of cluster leaders. Our simulation showed that the virtual structure produced by Selfadjust is better in terms of cluster radius and the location of cluster leaders. However, due to the selfadjusting nature of the algorithm, it might take longer time to compute than the other two algorithms. I.