On Organizing Algorithms

EM Algorithms for Self-Organizing Maps

Self-organizing maps are popular algorithms for unsupervised learning and data visualization. Exploiting the link between vector quantization and mixture modeling, we derive EM algorithms for self-organizing maps with and without missing values. We compare self-organizing maps with the elastic-net approach and explain why the former is better suited for the visualization of high-dimensional dat...

Algorithms for Self-Organizing Wireless Sensor Networks

Self-Organizing Maps and Fuzzy C-Means Algorithms on Gait Analysis Based on Inertial Sensors Data

Human gait corresponds to the physiological way of locomotion, which can be affected by several injuries. Thus, gait analysis plays an important role in observing kinematic and kinetic parameters of the joints involved with such movement pattern. Due to the complexity of such analysis, this paper explores the performance of two adaptive methods, Fuzzy c-means (FCM) and Self-organizing maps (SOM...

Efficient clustering algorithms for self-organizing wireless sensor networks

Self-organization of wireless sensor networks, which involves network decomposition into connected clusters, is a challenging task because of the limited bandwidth and energy resources available in these networks. In this paper, we make contributions towards improving the efficiency of self-organization in wireless sensor networks. We first present a novel approach for message-efficient cluster...

Automatic Clustering with Self-Organizing Maps and Genetic Algorithms

The analysis of data sets of unknown characteristics usually demands that subsets (or clusters) of the data are identified in such a way that the members of any one such cluster display common (in some sense) characteristics. In order to do this we must determine a) The number of clusters, b) The clusters themselves and c) The labeling of every element in the data set such that each element bel...