Clustering Quality and Topology Preservation in Fast Learning SOMs

Fiannaca, Antonino; Fatta, Giuseppe Di; Gaglio, Salvatore; Rizzo, Riccardo; Urso, Alfonso

doi:10.1007/978-3-540-87536-9_60

Cited by 3 publications

(4 citation statements)

References 27 publications

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“…The number of computed epochs and initialization of neuron weights was the same as in the first part of the experiment, see Sect. 4 As we can see in Tab. V, the acceleration of our improved algorithm is appreciable.…”

Section: The Second Part Of Experimentsmentioning

confidence: 75%

“…Several varieties of this SOM learning algorithm have been published to improve its computational efficiency; i.e. modification for a large dataset WEBSOM [12] or Fast Learning SOM algorithm (FLSOM) [4] for smaller datasets. Batch SOM Learning Algorithm is another commonly published variety, where the weights ⃗ w k (t) are updated only at the end of each epoch [11,15].…”

Section: Som Learning Algorithmsmentioning

confidence: 99%

“…The dataset contained records of two month's worth of requested activities (HTTP requests) from the NASA Kennedy Space Center WWW server in Florida. 4 Standard data preprocessing methods were applied to the obtained dataset. The records from search engines and spiders were removed, and only the web site browsing option was left (without download of pictures and icons, stylesheets, scripts etc.).…”

Section: Weblogs Datasetmentioning

confidence: 99%

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Effective Clustering Algorithm for High-Dimensional Sparse Data Based on Som

Martinovič¹,

Slaninová²,

Vojáček³

et al. 2013

NNW

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Abstract:Most of the traditional clustering algorithms are poor for clustering more complex structures other than the convex spherical sample space. In the past few years, several spectral clustering algorithms were proposed to cluster arbitrarily shaped data in various real applications. However, spectral clustering relies on the dataset where each cluster is approximately well separated to a certain extent. In the case that the cluster has an obvious inflection point within a non-convex space, the spectral clustering algorithm would mistakenly recognize one cluster to be different clusters. In this paper, we propose a novel spectral clustering algorithm called HSC combined with hierarchical method, which obviates the disadvantage of the spectral clustering by not using the misleading information of the noisy neighboring data points. The simple clustering procedure is applied to eliminate the misleading information, and thus the HSC algorithm could cluster both convex shaped data and arbitrarily shaped data more efficiently and accurately. The experiments on both synthetic data sets and real data sets show that HSC outperforms other popular clustering algorithms. Furthermore, we observed that HSC can also be used for the estimation of the number of clusters.

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Section: The Second Part Of Experimentsmentioning

confidence: 75%

Section: Som Learning Algorithmsmentioning

confidence: 99%

Section: Weblogs Datasetmentioning

confidence: 99%

See 1 more Smart Citation

Effective Clustering Algorithm for High-Dimensional Sparse Data Based on Som

Martinovič¹,

Slaninová²,

Vojáček³

et al. 2013

NNW

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show abstract

“…The learning process stops when the value of Δ QE is less than a user-defined threshold value. FLSOM was compared with other SOM-based algorithms, using both artificial and real biological datasets [ 8 , 14 ]. FLSOM provided a good convergence time and, most importantly, better results with respect to local distortion, topology preservation and clustering quality.…”

Section: Methodsmentioning

confidence: 99%

The BioDICE Taverna plugin for clustering and visualization of biological data: a workflow for molecular compounds exploration

et al. 2014

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BackgroundIn many experimental pipelines, clustering of multidimensional biological datasets is used to detect hidden structures in unlabelled input data. Taverna is a popular workflow management system that is used to design and execute scientific workflows and aid in silico experimentation. The availability of fast unsupervised methods for clustering and visualization in the Taverna platform is important to support a data-driven scientific discovery in complex and explorative bioinformatics applications.ResultsThis work presents a Taverna plugin, the Biological Data Interactive Clustering Explorer (BioDICE), that performs clustering of high-dimensional biological data and provides a nonlinear, topology preserving projection for the visualization of the input data and their similarities. The core algorithm in the BioDICE plugin is Fast Learning Self Organizing Map (FLSOM), which is an improved variant of the Self Organizing Map (SOM) algorithm. The plugin generates an interactive 2D map that allows the visual exploration of multidimensional data and the identification of groups of similar objects. The effectiveness of the plugin is demonstrated on a case study related to chemical compounds.ConclusionsThe number and variety of available tools and its extensibility have made Taverna a popular choice for the development of scientific data workflows. This work presents a novel plugin, BioDICE, which adds a data-driven knowledge discovery component to Taverna. BioDICE provides an effective and powerful clustering tool, which can be adopted for the explorative analysis of biological datasets.

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