The Experts below are selected from a list of 22341 Experts worldwide ranked by ideXlab platform
Juha Vesanto - One of the best experts on this subject based on the ideXlab platform.
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distance matrix based clustering of the Self Organizing Map
Lecture Notes in Computer Science, 2002Co-Authors: Juha Vesanto, Mika SulkavaAbstract:Clustering of data is one of the main applications of the Self-Organizing Map (SOM). U-matrix is a commonly used technique to cluster the SOM visually. However, in order to be really useful, clustering needs to be an automated process. There are several techniques which can be used to cluster the SOM autonomously, but the results they provide do not follow the results of U-matrix very well. In this paper, a clustering approach based on distance matrices is introduced which produces results which are very similar to the U-matrix. It is compared to other SOM-based clustering approaches.
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Clustering of the Self-Organizing Map
IEEE Transactions on Neural Networks, 2000Co-Authors: Juha Vesanto, Esa AlhoniemiAbstract:The Self-Organizing Map (SOM) is an excellent tool in exploratory phase of data mining. It projects input space on prototypes of a low-dimensional regular grid that can be effectively utilized to visualize and explore properties of the data. When the number of SOM units is large, to facilitate quantitative analysis of the Map and the data, similar units need to be grouped, i.e., clustered. In this paper, different approaches to clustering of the SOM are considered. In particular, the use of hierarchical agglomerative clustering and partitive clustering using k-means are investigated. The two-stage procedure-first using SOM to produce the prototypes that are then clustered in the second stage-is found to perform well when compared with direct clustering of the data and to reduce the computation time.
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Self Organizing Map in matlab the som toolbox
1999Co-Authors: Juha Vesanto, Esa Alhoniemi, Johan Himberg, Juha ParhankangasAbstract:The Self-Organizing Map (SOM) is a vector quantization method which places the prototype vectors on a regular low-dimensional grid in an ordered fashion. This makes the SOM a powerful visualization tool. The SOM Toolbox is an implementation of the SOM and its visualization in the Matlab 5 computing environment. In this article, the SOM Toolbox and its usage are shortly presented. Also its performance in terms of computational load is evaluated and compared to a corresponding Cprogram.
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process monitoring and modeling using the Self Organizing Map
Computer-Aided Engineering, 1999Co-Authors: Esa Alhoniemi, Olli Simula, Jaakko Hollmen, Juha VesantoAbstract:The Self-Organizing Map (SOM) is a powerful neural network method for analysis and visualization of high-dimensional data. It Maps nonlinear statistical dependencies between high-dimensional measurement data into simple geometric rela- tionships on a usually two-dimensional grid. The Mapping roughly preserves the most important topological and metric relationships of the original data elements and, thus, inherently clusters the data. The need for visualization and clustering occurs, for instance, in the analysis of various engineering problems. In this paper, the SOM has been applied in monitoring and modeling of complex industrial processes. Case studies, including pulp process, steel production, and paper industry are described.
Hiroomi Hikawa - One of the best experts on this subject based on the ideXlab platform.
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2005 special issue fpga implementation of Self Organizing Map with digital phase locked loops
Neural Networks, 2005Co-Authors: Hiroomi HikawaAbstract:The Self-Organizing Map (SOM) has found applicability in a wide range of application areas. Recently new SOM hardware with phase modulated pulse signal and digital phase-locked loops (DPLLs) has been proposed (Hikawa, 2005). The system uses the DPLL as a computing element since the operation of the DPLL is very similar to that of SOM's computation. The system also uses square waveform phase to hold the value of the each input vector element. This paper discuss the hardware implementation of the DPLL SOM architecture. For effective hardware implementation, some components are redesigned to reduce the circuit size. The proposed SOM architecture is described in VHDL and implemented on field programmable gate array (FPGA). Its feasibility is verified by experiments. Results show that the proposed SOM implemented on the FPGA has a good quantization capability, and its circuit size very small. .
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fpga implementation of Self Organizing Map with digital phase locked loops
International Joint Conference on Neural Network, 2005Co-Authors: Hiroomi HikawaAbstract:The Self-Organizing Map (SOM) has found applicability in a wide range of application areas. Recently new SOM hardware with phase modulated pulse signal and digital phase-locked loops (DPLLs) has been proposed (Hikawa, 2005). The system uses the DPLL as a computing element since the operation of the DPLL is very similar to that of SOM's computation. The system also uses square waveform phase to hold the value of the each input vector element. This paper discuss the hardware implementation of the DPLL SOM architecture. For effective hardware implementation, some components are redesigned to reduce the circuit size. The proposed SOM architecture is described in VHDL and implemented on field programmable gate array (FPGA). Its feasibility is verified by experiments. Results show that the proposed SOM implemented on the FPGA has a good quantization capability, and its circuit size very small.
Esa Alhoniemi - One of the best experts on this subject based on the ideXlab platform.
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Clustering of the Self-Organizing Map
IEEE Transactions on Neural Networks, 2000Co-Authors: Juha Vesanto, Esa AlhoniemiAbstract:The Self-Organizing Map (SOM) is an excellent tool in exploratory phase of data mining. It projects input space on prototypes of a low-dimensional regular grid that can be effectively utilized to visualize and explore properties of the data. When the number of SOM units is large, to facilitate quantitative analysis of the Map and the data, similar units need to be grouped, i.e., clustered. In this paper, different approaches to clustering of the SOM are considered. In particular, the use of hierarchical agglomerative clustering and partitive clustering using k-means are investigated. The two-stage procedure-first using SOM to produce the prototypes that are then clustered in the second stage-is found to perform well when compared with direct clustering of the data and to reduce the computation time.
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Self Organizing Map in matlab the som toolbox
1999Co-Authors: Juha Vesanto, Esa Alhoniemi, Johan Himberg, Juha ParhankangasAbstract:The Self-Organizing Map (SOM) is a vector quantization method which places the prototype vectors on a regular low-dimensional grid in an ordered fashion. This makes the SOM a powerful visualization tool. The SOM Toolbox is an implementation of the SOM and its visualization in the Matlab 5 computing environment. In this article, the SOM Toolbox and its usage are shortly presented. Also its performance in terms of computational load is evaluated and compared to a corresponding Cprogram.
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process monitoring and modeling using the Self Organizing Map
Computer-Aided Engineering, 1999Co-Authors: Esa Alhoniemi, Olli Simula, Jaakko Hollmen, Juha VesantoAbstract:The Self-Organizing Map (SOM) is a powerful neural network method for analysis and visualization of high-dimensional data. It Maps nonlinear statistical dependencies between high-dimensional measurement data into simple geometric rela- tionships on a usually two-dimensional grid. The Mapping roughly preserves the most important topological and metric relationships of the original data elements and, thus, inherently clusters the data. The need for visualization and clustering occurs, for instance, in the analysis of various engineering problems. In this paper, the SOM has been applied in monitoring and modeling of complex industrial processes. Case studies, including pulp process, steel production, and paper industry are described.
Michael Dittenbach - One of the best experts on this subject based on the ideXlab platform.
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the growing hierarchical Self Organizing Map exploratory analysis of high dimensional data
IEEE Transactions on Neural Networks, 2002Co-Authors: Andreas Rauber, Dieter Merkl, Michael DittenbachAbstract:The Self-Organizing Map (SOM) is a very popular unsupervised neural-network model for the analysis of high-dimensional input data as in data mining applications. However, at least two limitations have to be noted, which are related to the static architecture of this model as well as to the limited capabilities for the representation of hierarchical relations of the data. With our novel growing hierarchical SOM (GHSOM) we address both limitations. The GHSOM is an artificial neural-network model with hierarchical architecture composed of independent growing SOMs. The motivation was to provide a model that adapts its architecture during its unsupervised training process according to the particular requirements of the input data. Furthermore, by providing a global orientation of the independently growing Maps in the individual layers of the hierarchy, navigation across branches is facilitated. The benefits of this novel neural network are a problem-dependent architecture and the intuitive representation of hierarchical relations in the data. This is especially appealing in explorative data mining applications, allowing the inherent structure of the data to unfold in a highly intuitive fashion.
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uncovering hierarchical structure in data using the growing hierarchical Self Organizing Map
Neurocomputing, 2002Co-Authors: Michael Dittenbach, Andreas Rauber, Dieter MerklAbstract:Abstract Discovering the inherent structure in data has become one of the major challenges in data mining applications. It requires stable and adaptive models that are capable of handling the typically very high-dimensional feature spaces. In particular, the representation of hierarchical relations and intuitively visible cluster boundaries are essential for a wide range of data mining applications. Current approaches based on neural networks hardly fulfill these requirements within a single model. In this paper we present the growing hierarchical Self-Organizing Map ( GHSOM ), a neural network model based on the Self-Organizing Map. The main feature of this novel architecture is its capability of growing both in terms of Map size as well as in a three-dimensional tree-structure in order to represent the hierarchical structure present in a data collection during an unsupervised training process. This capability, combined with the stability of the Self-Organizing Map for high-dimensional feature space representation, makes it an ideal tool for data analysis and exploration. We demonstrate the potential of the GHSOM with an application from the information retrieval domain, which is prototypical both of the high-dimensional feature spaces frequently encountered in today's applications as well as of the hierarchical nature of data.
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the growing hierarchical Self Organizing Map
International Joint Conference on Neural Network, 2000Co-Authors: Michael Dittenbach, Dieter Merkl, Andreas RauberAbstract:We present the growing hierarchical Self-Organizing Map. This dynamically growing neural network model evolves into a hierarchical structure according to the requirements of the input data during an unsupervised training process. We demonstrate the benefits of this novel neural network model by Organizing a real-world document collection according to their similarities.
Dieter Merkl - One of the best experts on this subject based on the ideXlab platform.
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the growing hierarchical Self Organizing Map exploratory analysis of high dimensional data
IEEE Transactions on Neural Networks, 2002Co-Authors: Andreas Rauber, Dieter Merkl, Michael DittenbachAbstract:The Self-Organizing Map (SOM) is a very popular unsupervised neural-network model for the analysis of high-dimensional input data as in data mining applications. However, at least two limitations have to be noted, which are related to the static architecture of this model as well as to the limited capabilities for the representation of hierarchical relations of the data. With our novel growing hierarchical SOM (GHSOM) we address both limitations. The GHSOM is an artificial neural-network model with hierarchical architecture composed of independent growing SOMs. The motivation was to provide a model that adapts its architecture during its unsupervised training process according to the particular requirements of the input data. Furthermore, by providing a global orientation of the independently growing Maps in the individual layers of the hierarchy, navigation across branches is facilitated. The benefits of this novel neural network are a problem-dependent architecture and the intuitive representation of hierarchical relations in the data. This is especially appealing in explorative data mining applications, allowing the inherent structure of the data to unfold in a highly intuitive fashion.
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uncovering hierarchical structure in data using the growing hierarchical Self Organizing Map
Neurocomputing, 2002Co-Authors: Michael Dittenbach, Andreas Rauber, Dieter MerklAbstract:Abstract Discovering the inherent structure in data has become one of the major challenges in data mining applications. It requires stable and adaptive models that are capable of handling the typically very high-dimensional feature spaces. In particular, the representation of hierarchical relations and intuitively visible cluster boundaries are essential for a wide range of data mining applications. Current approaches based on neural networks hardly fulfill these requirements within a single model. In this paper we present the growing hierarchical Self-Organizing Map ( GHSOM ), a neural network model based on the Self-Organizing Map. The main feature of this novel architecture is its capability of growing both in terms of Map size as well as in a three-dimensional tree-structure in order to represent the hierarchical structure present in a data collection during an unsupervised training process. This capability, combined with the stability of the Self-Organizing Map for high-dimensional feature space representation, makes it an ideal tool for data analysis and exploration. We demonstrate the potential of the GHSOM with an application from the information retrieval domain, which is prototypical both of the high-dimensional feature spaces frequently encountered in today's applications as well as of the hierarchical nature of data.
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the growing hierarchical Self Organizing Map
International Joint Conference on Neural Network, 2000Co-Authors: Michael Dittenbach, Dieter Merkl, Andreas RauberAbstract:We present the growing hierarchical Self-Organizing Map. This dynamically growing neural network model evolves into a hierarchical structure according to the requirements of the input data during an unsupervised training process. We demonstrate the benefits of this novel neural network model by Organizing a real-world document collection according to their similarities.
