It is compared to procedures frequently used in microarray analysis, such as hierarchical clustering, K-means, and self-organizing map. All algorithms are applied to both simulation study and the genes of , such as maximizing between clusters and minimizing within clusters. The Cureton and D'...
anotherarenotclusteredtogether.Clusteringcanbevisualizedasahypergraphpartitioningproblem[TN91].Thenodesofthegrapharetheobjects,andtheedgesrepresentthereferencesbetweenobjects.Ithasbeenshownthattheproblemof�ndingthebestcluster-ingisanNP-completeproblem[Hu91].Severalheuristicclusteringalgorithmshavebeendeveloped[Sta84,...
Comparison of clustering algorithms in the context of software evolution To aid software analysis and maintenance tasks, a number of software clustering algorithms have been proposed to automatically partition a software system ... J Wu,AE Hassan,RC Holt - IEEE 被引量: 149发表: 2005年 A Scalable...
As a result, finding appropriate algorithms in this field helps significantly to organize information and extract the correct answer from different queries of database. One of steps of duplicate detection is clustering. Clustering is a classification process of existing data sets into different clusters...
A Wireless Sensor Network (WSN) is composed of distributed sensors with limited processing capabilities and energy restrictions. These unique attributes pose new challenges amongst which prolonging the WSN lifetime is one of the most important. Clustering is an energy efficient routing technique that ha...
A roadmap of clustering algorithms: finding a match for a biomedical application Clustering is ubiquitously applied in bioinformatics with hierarchical clustering and k-means partitioning being the most popular methods. Numerous improve... A Bill,A An,X Wang,... - 《Briefings in Bioinformatics》 ...
We have implemented a number of heuristics for the consensus clustering problem, and here we compare their performance, independent of data size, in terms of ecacy and eciency, on both simulated and real data sets. We find that based on the underlying algorithms and their behavior in practice...
We have implemented a number of heuristics for the consensus clustering problem, and here we compare their performance, independent of data size, in terms of efficacy and efficiency, on both simulated and real data sets. We find that based on the underlying algorithms and their behavior in ...
Because of the amount of information produced by such analyses and the errors carried by the reconstruction step, it is necessary to simplify this output. Clustering algorithms can be used to group samples that are similar according to a given metric. We propose to explore the well-known ...
Most of clustering algorithms based on natural computation aim to find the proper partition of data to be processed by optimizing certain criteria, so–called as cluster validity index, which must be effective and can reflect a similarity measure among objects properly. Up to now, four typical cl...