A parallel Kohonen algorithm for the classification of large spatial datasets

Openshaw, Stan; Turton, Ian

doi:10.1016/s0098-3004(96)00040-4

Cited by 29 publications

(12 citation statements)

References 4 publications

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“…Each of these provides excellent results but requires processing an input space a piece at a time rather that all at once; varies in the degree of approximating closeness and density of elements; has little to moderate opportunity for parallel computation; and reaches convergence after tens or hundreds of thousands of epochs. Several versions of the SOM involving parallel computing [1][2][3][4][5] and parallel algorithms [6][7][8] have been achieved which succeed in reducing computation time for large data sets, but theoretically have a small degree of parallelism with respect to neuron independence, and so remain largely sequential algorithms.…”

Section: Overview Of Self-organizing Mapsmentioning

confidence: 99%

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A Parallel Implementation of a Growing SOM Promoting Independent Neural Networks over Distributed Input Space

Hammond

MacClean

Valova

2006

The 2006 IEEE International Joint Conference on Neural Network Proceedings

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Self-Organizing maps can discover topological and multidimensional patterns using a variety of methods. We apply a parallel algorithm proposed by the authors (ParaSOM), which yields closer and denser approximations than other methods in a fraction of iterations, to a twodimensional pattern in a parallel environment to demonstrate a high degree of neuron independence. In a second implementation, pieces of a two-dimensional input space are distributed over a network and processed by independent ParaSOM algorithms.

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Section: Overview Of Self-organizing Mapsmentioning

confidence: 99%

“…Few versions of SOMs have been developed as parallel algorithms and/or implemented in a parallel environment. In each case only a moderate gain in performance was achieved [1][2][3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

A Parallel Implementation of a Growing SOM Promoting Independent Neural Networks over Distributed Input Space

Hammond

MacClean

Valova

2006

The 2006 IEEE International Joint Conference on Neural Network Proceedings

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“…SOM are very often used in problems of the analysis of large data structures e.g. in the problems of clustering or classification [9], [10], [11], [12], image processing [13], [14], [15], robotics [16], [17], time series forecasting [18], [19], [20] and faults detection and identification [21], [22], [23].…”

Section: Introductionmentioning

confidence: 99%

Faults diagnosis using self-organizing maps: A case study on the DAMADICS benchmark problem

Katunin

Amarowicz

Chrzanowski

2015

Annals of Computer Science and Information Systems

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Abstract-This paper deals with a method of faults detection and identification based on the clusterization of the multiple diagnostic signals. Various types of faults and character of their occurrence were simulated using DAMADICS Benchmark Process Control System. A great advantage of the applied approach based on self-organizing (Kohonen) maps is that even the smallest differences in signals allow for detection, isolation and identification of type of occurred faults with respect to the healthy condition of the investigated system based on the unsupervised learning. It was shown that in some cases the faults, which are undetectable during monitoring of simple heuristic and statistical parameters and other previously applied methods, are recognizable when the approach based on self-organizing maps is applied. The case studies presented in this paper show the faults detection procedure as well as clusterization of types and successful classification of almost all the unique faulty states of the investigated system.

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“…Openshaw [5] proposes a parallel algorithm for the classification of spatial datasets for the Cray T3D…”

Section: Introductionmentioning

confidence: 99%

Performance enhancement of a GIS-based facility location problem using desktop grid infrastructure

et al. 2013

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Alfonso Laguna, CD.; Hernández García, V. (2013). Performance enhancement of a GIS-based facility location problem using desktop grid infrastructure. Earth Science Informatics. 6(4):199-207. doi:10.1007/s12145-013-0119-1.Performance enhancement of a GIS-based facility location problem using desktop grid infrastructure Andrés García-García (a) , Carolina Perpiñá consisting of a set of the experiments on the case study, concludes that the desktop grid infrastructure is able to use a commercial GIS system to solve the spatial problem achieving high speedup and computational resource utilization. Particularly, the results of the experiments showed an increase in speedup of fourteen times using sixteen computers and a computational efficiency greater than 87% compared with the sequential procedure.

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A parallel Kohonen algorithm for the classification of large spatial datasets

Cited by 29 publications

References 4 publications

A Parallel Implementation of a Growing SOM Promoting Independent Neural Networks over Distributed Input Space

A Parallel Implementation of a Growing SOM Promoting Independent Neural Networks over Distributed Input Space

Faults diagnosis using self-organizing maps: A case study on the DAMADICS benchmark problem

Performance enhancement of a GIS-based facility location problem using desktop grid infrastructure

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