Learning the Number of Clusters in Self Organizing Map

Cabanès, Guénaël; Bennani, Younès

doi:10.5772/9164

Cited by 23 publications

(17 citation statements)

References 20 publications

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“…In this process it is necessary redundancy in the input data in order for the ANN to be able to detect patterns in the inputs. This analysis is particularly effective in the detection and classification of the data relevant groups (clusters), especially when there is no prior knowledge about what is being studied (Cabanes and Bennani, 2010;Qiao and Han, 2010). A detailed description of SOM algorithm was given in Kohonen (2001), Chon et al (1996) and Park et al (2003).…”

Section: Analysis Of Resultsmentioning

confidence: 99%

Video-tracking of zebrafish (Danio rerio) as a biological early warning system using two distinct artificial neural networks: Probabilistic neural network (PNN) and self-organizing map (SOM)

et al. 2015

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Section: Analysis Of Resultsmentioning

confidence: 99%

Video-tracking of zebrafish (Danio rerio) as a biological early warning system using two distinct artificial neural networks: Probabilistic neural network (PNN) and self-organizing map (SOM)

et al. 2015

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“…The algorithm combines cluster analysis and sequential data mining to detect temporal re-occurrences in massive flows of data. 19 This kind of machine learning enables the machine to predict future occurrences once it detects sequences of events that are similar to previous occurrences. When using the algorithm for the Sensitive Assembly, we wanted to be able to predict the near future occurrences of the wall structure.…”

Section: Methodsmentioning

confidence: 99%

Sensitive Assembly: Gamifying the design and assembly of façade wall prototypes

Savov

Tessmann

Nielsen

2016

International Journal of Architectural Computing

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The article describes a method for gamifying the design and assembly of computationally integrated structures built out of discrete identical blocks. As a case study, the interactive installation Sensitive Assembly was designed and built at the Digital Design Unit (Prof. Dr Oliver Tessmann) at the Technische Universität of Darmstadt and exhibited during the digital art festival NODE 2015 in Frankfurt in 2015. Sensitive Assembly invites people to play a Jenga-like game: starting from a solid wall, players are asked to remove and replace the installation’s building blocks to create windows to a nurturing light while challenging its stability. A computational system that senses the current state of the wall guides the physical interaction and predicts an approaching collapse or a new light beam breaking through. The installation extends the notion of real-time feedback from the digital into the physical and uses machine-learning techniques to predict future structural behaviour.

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“…Each of them is good (or even optimal) under a number of conditions, but there are also disadvantages [2] is The Suffix Tree Clustering method [7] involves reprocessing the texts of documents. DS2L-SOM algorithm (Density-based Simultaneous Two-Level -SOM) presented in [8], the number of clusters obtained by various clustering methods (SSL = SOM+SingleLinkage, SKM = SOM+K-means). It is clear that the number of clusters is usually unknown factor that needs to be either stated by users based on their prior knowledge or expected in a certain way.…”

Section: Fig1 Sample Of Clustering Process (Proximity Matrix)mentioning

confidence: 99%

Innovative Algorithm for Managing the Number of Clusters

Shannaq*,

Shamsi,

AlAzzawi

2020

IJRTE

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This research work proposed an integrated approach using Fuzzy Clustering to discover the optimal number of clusters. The proposed technique is a great technological innovation clustering algorithm in marketing and could be used to determine the best group of customers, similar items and products. The new approach can independently determine the initial distribution of cluster centers. The task of finding the number of clusters is converted into the task of determining the size of the neural network, which later translated to identify the optimal groups of clusters. This approach has been tested using four business data set and shows outstanding results compared to traditional approaches. The proposed method is able to find without any significant error the expected exact number of clusters. Further, we believe that this work is a business value to increase market efficiency in finding out what group of clusters is more cost-effective.

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Learning the Number of Clusters in Self Organizing Map

Cited by 23 publications

References 20 publications

Video-tracking of zebrafish (Danio rerio) as a biological early warning system using two distinct artificial neural networks: Probabilistic neural network (PNN) and self-organizing map (SOM)

Video-tracking of zebrafish (Danio rerio) as a biological early warning system using two distinct artificial neural networks: Probabilistic neural network (PNN) and self-organizing map (SOM)

Sensitive Assembly: Gamifying the design and assembly of façade wall prototypes

Innovative Algorithm for Managing the Number of Clusters

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