Data mining techniques (5 articles)

A discussion on visual interactive data, exploration using self-organizing maps

Moehrmann, J., Burkovski, A., Baranovskiy, E., Heinze, G. A., Rapoport, A., & Heidemann, G. (2011, June). In International Workshop on Self-Organizing Maps (pp. 178-187). Springer, Berlin, Heidelberg.

This article provides an overview of state-of-the-art software tools for self-organizing map-based visual data exploration. Viscovery SOMine gets best grades for data preprocessing and interaction with the map and above average grades for interaction with data and visualization, as well as label assignment.

Characteristic-based clustering for time series data

Wang, X., Smith, K., & Hyndman, R. (2006). Data mining and knowledge Discovery, 13(3), 335-364.

This paper proposes a feature-engineering method for clustering time series based on their structural characteristics. Viscovery SOMine's SOM-Ward algorithm is used alongside complete linkage, k-means and fuzzy c-means clustering to test the generated features.

Data visualization of asymmetric data using Sammon mapping and applications of self-organizing maps

Li, H. (2005).

The performance of several software implementations of methods based on self-organizing maps is evaluated. Viscovery SOMine is found to be helpful in determining the number of clusters and recovering the cluster structure of data sets. A genocide and politicide data set is analyzed using Viscovery SOMine, followed by another analysis using public and private college data sets with the goal to identify schools with best values.

A scalable method for time series clustering

Wang, X., Smith, K. A., Hyndman, R., & Alahakoon, D. (2004). Technical Report, Monash University.

Global measures to compare (long) time series are introduced. The self-organizing map is used for additional dimension reduction and, finally, the time series are clustered using Viscovery's SOM-Ward algorithm.

A comparison of software implementations of SOM clustering procedures

Li, H., Golden, B., Wasil, E., Zantek, P. (2002). In Intelligent engineering systems through artificial neural networks; 12; 447-452. 12th, Artificial neural networks in engineering conference; 2002; St Louis, MO.

This review presentation compares the clustering possibilities of Viscovery SOMine with those in SOM_PAK and k-means clustering implemented in the SPSS Clementine software package. The Ward algorithm of Viscovery SOMine and its modified version resulted in the best cluster recovery rates and Rand statistic values of all considered methods.