Despite its wide applications as a tool for feature extraction, the Self‐Organizing Map (SOM)
remains a black box to most meteorologists and oceanographers. This paper evaluates the …

We survey agglomerative hierarchical clustering algorithms and discuss efficient
implementations that are available in R and other software environments. We look at …

The Meyal oil field (MOF) is among the most important contributors to Pakistan's oil and gas
industry. Northern Pakistan's Potwar Basin is located in the foreland and thrust bands of the …

Over the last decade, technological advances have generated an explosion of data with
substantially smaller sample size relative to the number of covariates (p≫ n). A common …

Developed by Jean-Paul Benzerci more than 30 years ago, correspondence analysis as a
framework for analyzing data quickly found widespread popularity in Europe. The topicality …

A data analysis method is proposed to cluster and explore spatio-temporal characteristics of
the 22 years of precipitation data (1982–2003) for Taiwan. The wavelet transform self …

This open access book covers aspects of unsupervised machine learning used for
knowledge discovery in data science and introduces a data-driven approach to cluster …

In this paper we propose a methodology for comparing clustering methods based on the
quality of the result and the performance of the execution. We applied it to several known …

Background High-dimensional biomedical data are frequently clustered to identify subgroup
structures pointing at distinct disease subtypes. It is crucial that the used cluster algorithm …

Variations in the spectral content of volcano seismicity related to changes in volcanic activity
are commonly identified manually in spectrograms. However, long time series of monitoring …