The goal of supervised learning is to build a concise model of the distribution of class labels
in terms of predictor features. The resulting classifier is then used to assign class labels to …

Mixed data comprises both numeric and categorical features, and mixed datasets occur
frequently in many domains, such as health, finance, and marketing. Clustering is often …

Multidimensional scaling (MDS) is a technique used to extract a set of independent
variables from a proximity matrix or matrices. Applications of MDS are found in a wide range …

We propose a neural network-based feature selection (FS) scheme that can control the level
of redundancy in the selected features by integrating two penalties into a single objective …

For many years, artificial neural networks (ANNs) have been studied and used to model
information processing systems based on or inspired by biological neural structures. They …

A novel multilevel hierarchical Kohonen Net (K-Map) for an intrusion detection system is
presented. Each level of the hierarchical map is modeled as a simple winner-take-all K-Map …

Visual analytics for time series data has received a considerable amount of attention.
Different approaches have been developed to understand the characteristics of the data and …

The self-organizing map (SOM) is a powerful method for visualization, cluster extraction, and
data mining. It has been used successfully for data of high dimensionality and complexity …

Finding the largest consensus set is one of the key ideas used by the original RANSAC for
removing outliers in robust-estimation. However, because of its random and non …

The discovery, interpretation, and presentation of multivariate spatial patterns are important
for scientific understanding of complex geographic problems. This research integrates …