Sentence examples for several clustering methods from inspiring English sources

QIIME implements several clustering methods to perform this operation.

We applied several clustering methods including K-means, hierarchical and principle component analysis on H3K4me2 ChIP-seq data from embryonic stem cell, neural progenitor cell and whole brain of mouse, trying to identify genes with the H3K4me2 binding on the gene body region in different cell development stage and study their redistribution in different tissue development stages.

Over the years, several clustering methods have been proposed for regional analysis.

A feature selection framework is developed in order to improve the clustering accuracy and reduce computational complexity.1 Several clustering methods such as K-Means, Affinity Propagation, Mean Shift and Spectral Clustering were applied.

Leukemia patients The Golub et al. dataset has served as a benchmark for several clustering methods [17] [19].

Cell cycle gene expression data obtained from Hela cells have been analyzed with several clustering methods and the genes organized into functional and regulatory groups [1], [2].

Furthermore, this approach uniquely employs a combination of several gene clustering methods based on different protein family and domain characterizations, thus provide complementary, often more informative, views on functional relationships.

We show that these methods outperform several other published clustering methods in terms of correctly predicting a classifier and especially in terms of the correctness of the provided prediction.

We compare performance of our method with that of several state-of-art clustering methods, which are categorized into two groups.

Among them, the use of sampling techniques has proved to be especially useful for data clustering methods that perform several iterations considering different initializations, such as CLARANS [19] and PAM [2].

In order to surmount this difficulty, the toolbox integrates several metaheuristics (Egea et al., 2007), clustering methods (Csendes et al., 2008) and sequential hybrids (Balsa-Canto et al., 2005, 2008b), which combine different mechanisms of global exploration of the search space with the use of local methods to enhance computational efficiency.