Methods of dimensionality reduction provide a way to understand and visualize the structure
of complex data sets. Traditional methods like principal component analysis and classical …

We present a new strategy called" curvilinear component analysis"(CCA) for dimensionality
reduction and representation of multidimensional data sets. The principle of CCA is a self …

It is common practice to evaluate the strength of forecasting methods using collections of
well-studied time series datasets, such as the M3 data. The question is, though, how diverse …

Self-organizing maps (SOM) have proven to be of significant economic value in the areas of
finance, economic and marketing applications. As a result, this area is rapidly becoming a …

Human participation plays an essential role in most decisions when analyzing data. The
huge storage capacity and computational power of computers cannot replace the human …

The SOM algorithm is very astonishing. On the one hand, it is very simple to write down and
to simulate, its practical properties are clear and easy to observe. However, on the other …

Self-Organizing Maps have a wide range of beneficial properties for data mining, like vector
quantization and projection. Several measures exist that quantify the quality of either of …

The SOM has several beneficial features which make it a useful method for data mining. One
of the most important features is the ability to preserve the topology in the projection. There …

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 …

Our confidence in the future performance of any algorithm, including optimization algorithms,
depends on how carefully we select test instances so that the generalization of algorithm …