Relational Neural Gas

Hammer, Barbara; Hasenfuss, Alexander

doi:10.1007/978-3-540-74565-5_16

Cited by 21 publications

(17 citation statements)

References 26 publications

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“…Furthermore we have compared these methods to the basic approach to arbitrarily choose some samples out of all samples and cluster them using KFCMEANS. For all experiments we have set the neighborhood range λ for RNG to be exponentially falling from N 2 to 0.01 which are stable standard values (see [9]). The fuzzifier for KFCMEANS had been set to 2.0 for the synthetic dataset and 1.25 for both other datasets to reach more hard than soft memberhips.…”

Section: Experiments and Resultsmentioning

confidence: 99%

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Parallelized Kernel Patch Clustering

Faußer

Schwenker

2010

Lecture Notes in Computer Science

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Abstract. Kernel based clustering methods allow to unsupervised partition samples in feature space but have a quadratic computation time O(n 2 ) where n are the number of samples. Therefore these methods are generally ineligible for large datasets. In this paper we propose a metaalgorithm that performs parallelized clusterings of subsets of the samples and merges them repeatedly. The algorithm is able to use many Kernel based clustering methods where we mainly emphasize on Kernel Fuzzy C-Means and Relational Neural Gas. We show that the computation time of this algorithm is basicly linear, i.e. O(n). Further we statistically evaluate the performance of this meta-algorithm on a real-life dataset, namely the Enron Emails.

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Section: Experiments and Resultsmentioning

confidence: 99%

“…For Weighted Kernel Batch Neural Gas or Weighted Relational Neural Gas [9] the assignment update step is:…”

Section: Kernel Based Methods For Clusteringmentioning

confidence: 99%

Parallelized Kernel Patch Clustering

Faußer

Schwenker

2010

Lecture Notes in Computer Science

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“…Relational Neural Gas (RNG) [7] overcomes the problem of discrete adaptation steps by using convex combinations of Euclidean embedded data points as prototypes. For that purpose, we assume that there exists a set of (in general unknown and presumably high dimensional) Euclidean points V such that…”

Section: Relational Neural Gasmentioning

confidence: 99%

“…Although a variety of methods which can directly work with relational data based on general principles such as extensions of the self-organizing map and neural gas have been proposed [11,3,7], these methods are not suited for huge data sets. For complex metrics such as alignment of DNA strings or complex kernels for text data, it is infeasable to compute all pairs of the distance matrix and at most a small fraction can effectively be addressed.…”

Section: Introductionmentioning

confidence: 99%

“…In this work, we present a new technique based on the Relational Neural Gas approach [7] that is able to handle this situation by a single pass technique based on patches that can be chosen in accordance to the size of the available randomaccess memory. This results in a linear time and finite memory algorithm for general dissimilarity data which shares the intuitivity and robustness of NG.…”

Section: Introductionmentioning

confidence: 99%

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Patch Relational Neural Gas – Clustering of Huge Dissimilarity Datasets

Hasenfuss

Hammer

Rossi

Artificial Neural Networks in Pattern Recognition

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Abstract. Clustering constitutes an ubiquitous problem when dealing with huge data sets for data compression, visualization, or preprocessing. Prototype-based neural methods such as neural gas or the self-organizing map offer an intuitive and fast variant which represents data by means of typical representatives, thereby running in linear time. Recently, an extension of these methods towards relational clustering has been proposed which can handle general non-vectorial data characterized by dissimilarities only, such as alignment or general kernels. This extension, relational neural gas, is directly applicable in important domains such as bioinformatics or text clustering. However, it is quadratic in m both in memory and in time (m being the number of data points). Hence, it is infeasible for huge data sets. In this contribution we introduce an approximate patch version of relational neural gas which relies on the same cost function but it dramatically reduces time and memory requirements. It offers a single pass clustering algorithm for huge data sets, running in constant space and linear time only.

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Dyconnmap: Dynamic connectome mapping—A neuroimaging python module

Marimpis

Dimitriadis

Goebel

2021

Human Brain Mapping

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Despite recent progress in the analysis of neuroimaging data sets, our comprehension of the main mechanisms and principles which govern human brain cognition and function remains incomplete. Network neuroscience makes substantial efforts to manipulate these challenges and provide real answers. For the last decade, researchers have been modelling brain structure and function via a graph or network that comprises brain regions that are either anatomically connected via tracts or functionally via a more extensive repertoire of functional associations. Network neuroscience is a relatively new multidisciplinary scientific avenue of the study of complex systems by pursuing novel ways to analyze, map, store and model the essential elements and their interactions in complex neurobiological systems, particularly the human brain, the most complex system in nature. Due to a rapid expansion of neuroimaging data sets' size and complexity, it is essential to propose and adopt new empirical tools to track dynamic patterns between neurons and brain areas and create comprehensive maps. In recent years, there is a rapid growth of scientific interest in moving functional neuroimaging analysis beyond simplified group or time-averaged approaches and sophisticated algorithms that can capture the time-varying properties of functional connectivity. We describe algorithms and network metrics that can capture the dynamic evolution of functional connectivity under this perspective. We adopt the word 'chronnectome' (integration of the Greek word 'Chronos', which means time, and connectome) to describe this specific branch of network neuroscience that explores how mutually informed brain activity correlates across time and brain space in a functional way. We also describe how good temporal mining of temporally evolved dynamic functional networks could give rise to the detection of specific brain states over which our brain evolved. This characteristic supports our complex

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Relational Neural Gas

Cited by 21 publications

References 26 publications

Parallelized Kernel Patch Clustering

Parallelized Kernel Patch Clustering

Patch Relational Neural Gas – Clustering of Huge Dissimilarity Datasets

Dyconnmap: Dynamic connectome mapping—A neuroimaging python module

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