Projection-Based Clustering through Self-Organization and Swarm Intelligence: Combining Cluster Analysis with the Visualization of High-Dimensional Data

This book covers aspects of unsupervised machine learning used for knowledge discovery in data science and introduces a data-driven approach to cluster analysis, the Databionic swarm (DBS). DBS consists of the 3D landscape visualization and clustering of data. The 3D landscape enables 3D printing of high-dimensional data structures.The clustering and number of clusters or an absence of cluster structure are verified by the 3D landscape at a glance. DBS is the first swarm-based technique that shows emergent properties while exploiting concepts of swarm intelligence, self-organization and the Nash equilibrium concept from game theory. It results in the elimination of a global objective function and the setting of parameters. By downloading the R package DBS can be applied to data drawn from diverse research fields and used even by non-professionals in the field of data mining

Projection-Based Clustering through Self-Organization and Swarm Intelligence: Combining Cluster Analysis with the Visualization of High-Dimensional Data

Cluster Analysis; Dimensionality Reduction; Swarm Intelligence; Visualization; Unsupervised Machine Learning; Data Science; Knowledge Discovery; 3D Printing; Self-Organization; Emergence; Game Theory; Advanced Analytics; High-Dimensional Data; Multivariate Data; Analysis of Structured Dat

Volcanic Seismic Events Classification using Unsupervised Learning Models

This paper explores the use of six different clustering-based classifiers to categorize two different volcanic seismic events and to find possible overlapping signals that could occur at the same time or immediately after seismic events occurrence. According to the explored classifiers space, only one out of 27 models was selected using the first selection criteria. Afterward, the Spectral Clustering classifier with k=2 was chosen as the best model, reaching an accuracy score of $92%...Este documento explora el uso de seis diferentes clasificadores basados en clustering, para categorizar dos diferentes eventos sísmico-volcánicos y encontrar posibles señales solapadas que pueden ocurrir al mismo tiempo o inmediatamente después de la aparición de eventos sísmicos. De acuerdo con el espacio de clasificadores explorado, el spectral-clustering con k=2 fue escogido como el mejor modelo, alcanzando una precisión del 92%..

Volcanic Seismic Events Classification using Unsupervised Learning Models

This paper explores the use of six different clustering-based classifiers to categorize two different volcanic seismic events and to find possible overlapping signals that could occur at the same time or immediately after seismic events occurrence. According to the explored classifiers space, only one out of 27 models was selected using the first selection criteria. Afterward, the Spectral Clustering classifier with k=2 was chosen as the best model, reaching an accuracy score of 92%...Este documento explora el uso de seis diferentes clasificadores basados en clustering, para categorizar dos diferentes eventos sísmico-volcánicos y encontrar posibles señales solapadas que pueden ocurrir al mismo tiempo o inmediatamente después de la aparición de eventos sísmicos. De acuerdo con el espacio de clasificadores explorado, el spectral-clustering con k=2 fue escogido como el mejor modelo, alcanzando una precisión del 92%. Este resultado representa un desempeño satisfactorio y competitivo en cuanto a clasificación, comparado con los métodos señalados en el estado de arte..

Projection-Based Clustering through Self-Organization and Swarm Intelligence: Combining Cluster Analysis with the Visualization of High-Dimensional Data

Cluster Analysis; Dimensionality Reduction; Swarm Intelligence; Visualization; Unsupervised Machine Learning; Data Science; Knowledge Discovery; 3D Printing; Self-Organization; Emergence; Game Theory; Advanced Analytics; High-Dimensional Data; Multivariate Data; Analysis of Structured Dat