Sentence examples for use of clustering techniques from inspiring English sources

We propose a methodology based on the use of clustering techniques derived from data analysis and multi-attribute decision analysis methods aiming at purposeful multidimensional poverty measurement.

Improvements in the application of the fundamental chemometric techniques are suggested – the design to collect representative samples, the careful use of clustering techniques, the evaluation of the uncertainty in classification parameters, and the evaluation of the effect of noisy information.

The use of clustering techniques and other ideas from areas such as computer science, machine learning, and uncertainty quantification — along with mathematical and statistical models — are often very useful for data analysis (see, e.g., [1 4] and many other references).

First, we propose a framework that uses a combination of general noise SVR models with naive online R minimization algorithm (NORMA) as optimization method, and then gives non-constant error intervals dependent upon input data aided by the use of clustering techniques.

Several approaches to microarray data analysis make use of clustering techniques [1] [4] to suggest functional roles for previously uncharacterized genes.

Methods of the third category, which are in the focus of this project, are based on pairwise comparisons of full-length protein sequences and typically involve the use of clustering techniques [19], [20].

Many different types of clustering techniques for chemical structures have been used in the literature [6 13].

Therefore, there has been a great deal of interest in the use of compound clustering techniques to aid in the selection of a representative subset of all the compounds available [3].

Many of these studies use clustering techniques [ 18- 23] to group tumours with similar gene expression patterns, and thus to identify clinically relevant molecular biomarkers for tumour classification.

An important goal of our research is to use clustering techniques to recognize the optimal location of a new well based on 3-D seismic data and available production-log data.

For example, Martin et al. [ 178] broke down a 34,000-probe microarray gene expression dataset into 23 sets of metagenes using clustering techniques.