Algorithms and Organizing

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

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...

Empire of Colonies: Self-stabilizing and Self-organizing Distributed Algorithms

Self-stabilization ensures automatic recovery from an arbitrary state; we define self-organization as a property of algorithms which display local attributes. More precisely, we say that an algorithm is self-organizing if (1) it converges in sublinear time and (2) reacts “fast” to topology changes. If s(n) is an upper bound on the convergence time and d(n) is an upper bound on the convergence t...

Birthday Paradox, Coupon Collectors, Caching Algorithms and Self-Organizing Search

This paper introduces a unified framework for the analysis of a class of random allocation processes thdt include: (i) the birthday paradox; (ii) the coupon collector problem* (iii) least-tecently-used (LRU) caching in memory management systems under the independent reference model; (iv) the move-to-front heuristic of self-organizing search. All analyses are relative to general nonuniform f rob...