Trend Clustering using Self Organizing Maps
A new clustering method using an augmentation to the self organizing maps
... between distances assigned to Cluster i and Cluster i+1 became the same for all values of i. However, in some datasets the clusters could be present in an uneven order. The above mentioned regular order would hinder the ...
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Self-Organizing Maps for clustering and visualization of bipartite graphs
... and clustering the nodes of a peculiar class of graphs: bipartite ...a self-organizing map algorithm and relies on an extension of this approach to data described by a dissimilarity ...
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Data Clustering and Topology Preservation Using 3D Visualization of Self Organizing Maps
... data using its xyz-axis output neuron’s arrangement or ...for clustering data that might not be obviously clustered neither easily interpreted using the traditional 2D-SOM such as the data that could ...
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A fuzzy logic-based representation for web page clustering using self-organizing maps
... Resumen: En este trabajo se eval´ ua un modelo de representaci´ on de p´ aginas web para clustering de documentos por medio de mapas autoorganizativos (SOM). Esta representaci´ on pretende reproducir o modelar en ...
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Big Data Clustering Using Heuristic Data Intensive Computing and Self Organizing Maps
... data clustering algorithms are having pitfalls while discovering efficient ...data clustering using a Heuristic data intensive computing (HDIC) and Self- Organizing Maps ...
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Using self organizing maps on compositional data
... Self–organizing maps (Kohonen 1997) is a type of artificial neural network developed to explore patterns in high–dimensional multivariate data. The conventional version of the algorithm involves the ...
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Face Recognition Using Self-Organizing Maps
... In Ref. (Lam & Yan, 1998), an analytic-to-holistic approach is introduced for identification of faces at different perspective variations. The ORL-database is used in the experiments. Only one upright frontal face is ...
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Assessing the Reproducibility of Clustering of Molecular Dynamics Conformations on Self-Organizing Maps
... INSSQ represent tighter clusters. The INV learning rate factor produces larger values of the objective function when compared to those from SOMs trained using both the POWER and LINEAR. The algorithms POWER and ...
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A Modified Clustering Method Based on Self-Organizing Maps and Its Applications
... direct clustering methods with less computation ...for clustering the proto-clusters ...processing clustering on small scale vectors obtained from ...U*F clustering, which is also ...
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A New Approach to Hierarchical Clustering and Structuring of Data with Self-Organizing Maps
... small maps and a deep hierarchy, while large values will result in large maps with a flat hierarchical ...several maps until an appropriate value for τ 1 is ...
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Application of self-organizing maps to clustering of high-frequency financial data
... The electronic limit order book, further referred to as the limit order book or the book, is a mechanism for collecting, storing, and matching of buy and sell limit orders submitted by market participants, as well as a ...
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Enhanced data clustering and classification using auto-associative neural networks and self organizing maps
... and clustering of highly non-linear multidimensional ...and Self Organizing Maps (SOM) for data clustering ...data clustering and classification performance by introducing novel ...
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Ice Shape Characterization Using Self-Organizing Maps
... shapes using a self-organizing map (SOM) technique is ...presented. Self-organizing maps are neural-network techniques for representing noisy, multi-dimensional data aligned ...
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Wave extreme characterization using self-organizing maps
... Morioka et al., 2010; Camus et al., 2011a; Falcieri et al., 2013). Typical applications of SOM are vector quantization, regression and clustering. SOMs gained credit among other techniques with same applications ...
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Mapping model behaviour using Self Organizing Maps
... that using the Signature Indices has positive effects on the way the SOM can be used to discriminate be- tween different model realizations which consequently bring about improvements regarding the use of the SOM ...
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Unsupervised feature learning using self-organizing maps.
... To investigate the contribution of each FLU in the overall process, we evaluated the method by excluding different subsets of FLUs. The worst result is obtained without any FLU, just applying the K-means ...
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Visualization of Agriculture Data Using Self-Organizing Maps
... Data Using Self-Organizing Maps Georg Ruß, Rudolf Kruse, Martin Schneider, Peter Wagner Abstract The importance of carrying out effective and sustainable agriculture is getting more and more ...
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Unsupervised clustering of IoT signals through feature extraction and self organizing maps
... the maps size have been increased to 15 × 15 which corresponds to 225 neurons and to 20 × 20 which cor- responds to 400 neurons ...bigger maps the SOM starts to over- represent the data distribution and as ...
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Metabolite-based clustering and visualization of mass spectrometry data using one-dimensional self-organizing maps
... For direct comparison with the previous 1D-SOM results we performed an average linkage HCA/K-means cluster- ing with 33 prototypes using Euclidean distances. Figure 8 shows the pruned HCA dendrogram, the resulting ...
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Incremental clustering of sonar images using self-organizing maps combined with fuzzy adaptive resonance theory
... In this work, a new approach for unsupervised segmentation of sidescan sonar images is proposed. Our approach is based on the mixture of two neural network algorithms: the SOM and ART algorithms. The SOM algorithm is a ...
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