In fact, cluster analysis is not as much a typical statistical test as it is a "collection" of different algorithms that "put objects into clusters according to well defined similarity rules". The point here is that, unlike many other statistical procedures, cluster analysis methods are mostly used when we do not have any a priori hypotheses, but are still in the exploratory phase of our research

Clustering techniques have been applied to a wide variety of research problems. Hartigan (1975) provides an excellent summary of the many published studies reporting the results of cluster analyses. For example, in the field of medicine , clustering diseases, cures for diseases, or symptoms of diseases can lead to very useful taxonomies. In the field of psychiatry, the correct diagnosis of clusters of symptoms such as paranoia, schizophrenia, etc. is essential for successful therapy. In archeology, researchers have attempted to establish taxonomies of stone tools, funeral objects, etc. by applying cluster analytic techniques. In general, whenever one needs to classify a "mountain" of information into manageable meaningful piles, cluster analysis is of great utility.

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• Statistical methods

1. Topics in Cluster Analysis
2. statistics
3. Standard Deviation
4. Variance

The standard deviation formula is very simple: it is the square root of the variance. It is the most commonly used measure of spread. The variance is computed as the average squared deviation of each number from its mean.

COLA is dedicated to understanding climate fluctuations on seasonal, interannual, and decadal scales, with special emphasis on the interactions between Earth's atmosphere, oceans, and land surfaces. By consolidating several research grants from three different federal agencies (NSF, NOAA and NASA), a single, multi-agency, multi-year research project was developed to create a critical mass of scientists working together as a team at COLA on the basic problem of the predictability of the present climate. With continuing multi-agency support, COLA has become a national center of excellence for research on climate variability and predictability. The goal of COLA research is to explore, establish and quantify the predictability and prediction of seasonal to interannual variability of the present climate through the use of state-of-the-art dynamical coupled ocean-atmosphere general circulation models and the development of new techniques for analysis of observational and model data.