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A Hybrid Parallel SOM Algorithm for Large Maps in DataMining
"... Abstract. We propose a method for a parallel implementation of the SelfOrganizing Map (SOM) algorithm, widely used in datamining. We call this method Hybrid in the sense that it combines the advantages of the common networkpartition and datapartition approaches, and is particularly effective whe ..."
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Abstract. We propose a method for a parallel implementation of the SelfOrganizing Map (SOM) algorithm, widely used in datamining. We call this method Hybrid in the sense that it combines the advantages of the common networkpartition and datapartition approaches, and is particularly effective when dealing with large maps. Based on the fact that a global topological ordering of the map is achieved in a short period of time, the proposed method obtains this ordering during the initial epochs using the Batch DataPartition algorithm. Here we calculate the input data histogram over the map, based on which the map is segmented and the respective input vectors redistributed equally. From now on, until new segmentation each node only processes their subset of samples in their region of the map. Our experimental results show an average speedup of 1.27 compared to the classical Batch datapartition method, while maintaining the topological information of the maps.
FEATURE CLUSTERINGWITH SELFORGANIZINGMAPS AND AN APPLICATION TO FINANCIAL TIMESERIES FOR PORTFOLIO SELECTION
"... Abstract: The portfolio selection is an important technique for decreasing the risk in the stock investment. In the portfolio selection, the investor’s property is distributed for a set of stocks in order to minimize the financial risk in market downturns. With this in mind, and aiming to develop a ..."
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Abstract: The portfolio selection is an important technique for decreasing the risk in the stock investment. In the portfolio selection, the investor’s property is distributed for a set of stocks in order to minimize the financial risk in market downturns. With this in mind, and aiming to develop a tool to assist the investor in finding balanced portoflios, we achieved a generic method for feature clustering with SelfOrganizing Maps (SOM). The ability of neural networks to discover nonlinear relationships in input data makes them ideal for modeling dynamic systems as the stock market. The method proposed makes use the remarkable visualization capabilities of the SOM, namely the Component Planes, to detect nonlinear correlations between features. An appropriate metric the improved Rv coefficient is also proposed to compare Component Planes and generate a distance matrix between features, after which an hierarchical clustering method is used to obtain the clusters of features. Results obtained are empirically sound, although at this moment we do not provide mathematical comparisons with other methods. Results also show that feature clustering with the SOM presents itself as a viable method to cluster timeseries. 1
Protection of Privacy in Distributed Databases using Clustering
"... Abstract: Clustering is the technique which discovers groups over huge amount of data, based on similarities, regardless of their structure (multidimensional or two dimensional). We applied an algorithm (DSOM) to cluster distributed datasets, based on selforganizing maps (SOM) and extends this app ..."
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Abstract: Clustering is the technique which discovers groups over huge amount of data, based on similarities, regardless of their structure (multidimensional or two dimensional). We applied an algorithm (DSOM) to cluster distributed datasets, based on selforganizing maps (SOM) and extends this approach presenting a strategy for efficient cluster analysis in distributed databases using SOM and Kmeans. The proposed strategy applies SOM algorithm separately in each distributed dataset, relative to database horizontal partitions, to obtain a representative subset of each local dataset. In the sequence, these representative subsets are sent to a central site, which performs a fusion of the partial results and applies SOM and Kmeans algorithms to obtain a final result. I.