PSS Business Case Map: Supporting Idea Generation in PSS Design

2012 ◽  
Author(s):  
Fumiya Akasaka ◽  
Kazuki Fujita ◽  
Yoshiki Shimomura
Keyword(s):  
Idea Generation ◽  
Business Case ◽  
Literature Survey ◽  
The Self ◽  
High Dimensional ◽  
Self Organizing Map ◽  
Two Dimensional ◽  
Service Type ◽  
Business Cases ◽  
Low Dimensional

This paper proposes the PSS Business Case Map as a tool to support designers’ idea generation in PSS design. The map visualizes the similarities among PSS business cases in a two-dimensional diagram. To make the map, PSS business cases are first collected by conducting, for example, a literature survey. The collected business cases are then classified from multiple aspects that characterize each case such as its product type, service type, target customer, and so on. Based on the results of this classification, the similarities among the cases are calculated and visualized by using the Self-Organizing Map (SOM) technique. A SOM is a type of artificial neural network that is trained using unsupervised learning to produce a low-dimensional (typically two-dimensional) view from high-dimensional data. The visualization result is offered to designers in a form of a two-dimensional map, which is called the PSS Business Case Map. By using the map, designers can figure out the position of their current business and can acquire ideas for the servitization of their business.

scholarly journals Assessing relationships between chromatin interactions and regulatory genomic activities using the self-organizing map

2020 ◽  
Author(s):  
Timothy Kunz ◽  
Lila Rieber ◽  
Shaun Mahony
Keyword(s):  
Genome Organization ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Interaction Space ◽  
The Self ◽  
Chromatin Interaction ◽  
High Dimensional ◽  
Self Organizing Map ◽  
Two Dimensional ◽  
Chromatin Interactions

ABSTRACTFew existing methods enable the visualization of relationships between regulatory genomic activities and genome organization as captured by Hi-C experimental data. Genome-wide Hi-C datasets are often displayed using “heatmap” matrices, but it is difficult to intuit from these heatmaps which biochemical activities are compartmentalized together. High-dimensional Hi-C data vectors can alternatively be projected onto three-dimensional space using dimensionality reduction techniques. The resulting three-dimensional structures can serve as scaffolds for projecting other forms of genomic information, thereby enabling the exploration of relationships between genome organization and various genome annotations. However, while three-dimensional models are contextually appropriate for chromatin interaction data, some analyses and visualizations may be more intuitively and conveniently performed in two-dimensional space.We present a novel approach to the visualization and analysis of chromatin organization based on the Self-Organizing Map (SOM). The SOM algorithm provides a two-dimensional manifold which adapts to represent the high dimensional chromatin interaction space. The resulting data structure can then be used to assess the relationships between regulatory genomic activities and chromatin interactions. For example, given a set of genomic coordinates corresponding to a given biochemical activity, the degree to which this activity is segregated or compartmentalized in chromatin interaction space can be intuitively visualized on the 2D SOM grid and quantified using Lorenz curve analysis. We demonstrate our approach for exploratory analysis of genome compartmentalization in a high-resolution Hi-C dataset from the human GM12878 cell line. Our SOM-based approach provides an intuitive visualization of the large-scale structure of Hi-C data and serves as a platform for integrative analyses of the relationships between various genomic activities and genome organization.

scholarly journals Subspace Clustering with Sparsity and Grouping Effect

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Binbin Zhang ◽  
Weiwei Wang ◽  
Xiangchu Feng
Keyword(s):  
State Of The Art ◽  
Subspace Clustering ◽  
The Self ◽  
Dimensional Structure ◽  
High Dimensional ◽  
Affinity Matrix ◽  
Grouping Effect ◽  
Art Methods ◽  
Data Points ◽  
Low Dimensional

Subspace clustering aims to group a set of data from a union of subspaces into the subspace from which it was drawn. It has become a popular method for recovering the low-dimensional structure underlying high-dimensional dataset. The state-of-the-art methods construct an affinity matrix based on the self-representation of the dataset and then use a spectral clustering method to obtain the final clustering result. These methods show that sparsity and grouping effect of the affinity matrix are important in recovering the low-dimensional structure. In this work, we propose a weighted sparse penalty and a weighted grouping effect penalty in modeling the self-representation of data points. The experimental results on Extended Yale B, USPS, and Berkeley 500 image segmentation datasets show that the proposed model is more effective than state-of-the-art methods in revealing the subspace structure underlying high-dimensional dataset.

Self-Organizing Map Learning Nonlinearly Embedded Manifolds

2005 ◽  
Vol 4 (1) ◽  
pp. 22-31 ◽  
Author(s):  
Timo Similä
Keyword(s):  
Learning Algorithm ◽  
Image Data ◽  
High Dimensional ◽  
Locally Linear Embedding ◽  
Complex Data ◽  
Dimensional Manifold ◽  
Self Organizing Map ◽  
Training Strategy ◽  
Low Dimensional ◽  
Self Organizing

One of the main tasks in exploratory data analysis is to create an appropriate representation for complex data. In this paper, the problem of creating a representation for observations lying on a low-dimensional manifold embedded in high-dimensional coordinates is considered. We propose a modification of the Self-organizing map (SOM) algorithm that is able to learn the manifold structure in the high-dimensional observation coordinates. Any manifold learning algorithm may be incorporated to the proposed training strategy to guide the map onto the manifold surface instead of becoming trapped in local minima. In this paper, the Locally linear embedding algorithm is adopted. We use the proposed method successfully on several data sets with manifold geometry including an illustrative example of a surface as well as image data. We also show with other experiments that the advantage of the method over the basic SOM is restricted to this specific type of data.

P1DSOM — A FAST SEARCH ALGORITHM FOR HIGH-DIMENSIONAL FEATURE SPACE PROBLEMS

2014 ◽  
Vol 28 (02) ◽  
pp. 1459005 ◽  
Author(s):  
ALAA SAGHEER ◽  
NAYOUKI TSURUTA ◽  
RIN-ICHIRO TANIGUCHI
Keyword(s):  
Search Algorithm ◽  
Computational Cost ◽  
Feature Space ◽  
The Self ◽  
Data Exploration ◽  
High Dimensional ◽  
Self Organizing Map ◽  
Fast Search Algorithm ◽  
Neural Network Algorithm ◽  
Hierarchical Som

The self-organizing map (SOM) is a traditional neural network algorithm used to achieve feature extraction, clustering, visualization and data exploration. However, it is known that the computational cost of the traditional SOM, used to search for the winner neuron, is expensive especially in case of treating high-dimensional data. In this paper, we propose a novel hierarchical SOM search algorithm which significantly reduces the expensive computational cost associated with traditional SOM. It is shown here that the computational cost of the proposed approach, compared to traditional SOM, to search for the winner neuron is reduced into O(D1 + D2 + ⋯ + DN) instead of O(D1 × D2 × ⋯ × DN), where Dj is the number of neurons through a dimension dj of the feature map. At the same time, the new algorithm maintains all merits and qualities of the traditional SOM. Experimental results show that the proposed algorithm is a good alternate to traditional SOM, especially, in high-dimensional feature space problems.

Study of Material Parameters’ Effect on Polymer Scratch Using SOM Method

2012 ◽  
Vol 452-453 ◽  
pp. 1420-1423
Author(s):  
Jian Wei Zhang ◽  
Han Jiang
Keyword(s):  
Coupling Effect ◽  
Polymeric Materials ◽  
Scratch Resistance ◽  
The Self ◽  
High Dimensional ◽  
Self Organizing Map ◽  
Material Parameters ◽  
Neural Network Algorithm ◽  
Statistical Relationships ◽  
Artificial Neural Network Algorithm

Due to its inherited complexity, the polymer material parameters’ effect on the scratch resistance is difficult to detect. Using the scratch experimental results of a set of polypropylene (PP), the Self-Organizing Map (SOM) method, an artificial neural network algorithm, was adopted to study the effect of various material parameters on polymer scratch. Especially suitable for the analysis of high-dimensional data with nonlinear statistical relationships, SOM method helps to find out the influence of different material parameters on scratch behavior. This information can be used to estimate the possible performance of polymeric materials to certain extent without extra scratch experimental work. It also helps researchers to decide which group of properties should be paid more attention when studying the coupling effect of material parameters on polymer scratch resistance.

scholarly journals Consequences of PCA graphs, SNP codings, and PCA variants for elucidating population structure

2018 ◽  
Author(s):  
Hugh G. Gauch ◽  
Sheng Qian ◽  
Hans-Peter Piepho ◽  
Linda Zhou ◽  
Rui Chen
Keyword(s):  
Population Structure ◽  
Rare Allele ◽  
Literature Survey ◽  
High Dimensional ◽  
Common Allele ◽  
Snp Data ◽  
The Common ◽  
Low Dimensional ◽  
Main Effects

AbstractSNP datasets are high-dimensional, often with thousands to millions of SNPs and hundreds to thousands of samples or individuals. Accordingly, PCA graphs are frequently used to provide a low-dimensional visualization in order to display and discover patterns in SNP data from humans, animals, plants, and microbes—especially to elucidate population structure. Given the popularity of PCA, one might expect that PCA is understood well and applied effectively. However, our literature survey of 125 representative articles that apply PCA to SNP data shows that three choices have usually been made poorly: PCA graph, SNP coding, and PCA variant. Our main three recommendations are simple and easily implemented: Use PCA biplots, SNP coding 1 for the rare allele and 0 for the common allele, and double-centered PCA (or AMMI1 if main effects are of interest). The ultimate benefit from informed and optimal choices of PCA graph, SNP coding, and PCA variant, is expected to be discovery of more biology, and thereby acceleration of medical, agricultural, and other vital applications.

The self-organizing map in synoptic climatological research

2011 ◽  
Vol 35 (1) ◽  
pp. 109-119 ◽  
Author(s):  
Scott C. Sheridan ◽  
Cameron C. Lee
Keyword(s):  
Spatial Patterns ◽  
The Self ◽  
Synoptic Climatology ◽  
Self Organizing Map ◽  
Two Dimensional ◽  
Clustering Methods ◽  
Traditional Methods ◽  
Self Organizing Maps ◽  
Dimensional Array ◽  
Self Organizing

Self-organizing maps (SOMs) are a relative newcomer to synoptic climatology; the method itself has only been utilized in the field for around a decade. In this article, we review the major developments and climatological applications of SOMs in the literature. The SOM can be used in synoptic climatological analysis in a manner similar to most other clustering methods. However, as the results from a SOM are generally represented by a two-dimensional array of cluster types that ‘self-organize’, the synoptic categories in the array effectively represent a continuum of synoptic categorizations, compared with discrete realizations produced through most traditional methods. Thus, a larger number of patterns can be more readily understood, and patterns, as well as transitional nodes between patterns, can be discerned. Perhaps the most intriguing development with SOMs has been the new avenues of visualization; the resultant spatial patterns of any variable can be more readily understood when displayed in a SOM. This improved visualization has led to SOMs becoming an increasingly popular tool in various research with climatological applications from other disciplines as well.

scholarly journals Classifying non-banking financial institutions based on their financial performance

2019 ◽  
Vol 1 (1) ◽  
pp. 185-193
Author(s):  
Adrian Costea
Keyword(s):  
Financial Institutions ◽  
Capital Adequacy ◽  
The Self ◽  
Three Dimensions ◽  
Self Organizing Map ◽  
Two Dimensional ◽  
Self Organizing Maps ◽  
Performance Dimensions ◽  
Self Organising Map ◽  
Self Organizing

Abstract In this paper we evaluate comparatively the performance of non-banking financial institutions in Romania by the means of unsupervised neural networks in terms of Kohonen’ Self-Organizing Maps algorithm. We create a benchmarking model in the form of a two-dimensional map (a self-organizing map) that can be used to assess visually the performance of non-banking financial institutions based on different performance dimensions, such as capital adequacy, assets’ quality and profitability. We use the following indicators: Equity ratio (Leverage) for the capital adequacy dimension, Loans granted to clients (net value) / total assets (net value) for the assets’ quality dimension and Return on assets (ROA) for the profitability dimension. We have excluded from our analysis the other three dimensions used in evaluating the performance of banks, due to lack of data (for the two qualitative dimensions: quality of ownership and management) and irrelevance with the NFIs’ sector (liquidity). The proposed model is based on the Self-Organising Map algorithm which creates a two-dimensional map (e.g. 6x4 = 24 neurons) from p-dimensional input data. The data were collected for eleven non-banking financial institutions for four years 2007-2010, in total 44 observations. Using the visualization capabilities of the Self-Organising Map model and the trajectories we show the movements of the three non-banking financial institutions with the worst performance: the largest underperformer denoted with X, the second largest underperformer denoted with Y and the third largest underperformer denoted with Z between 2007 and 2010.

Electronic properties of quasi two-dimensional transition metals chalcogenides with low-dimensional magnetism

Doklady BGUIR ◽  
2020 ◽  
Vol 18 (7) ◽  
pp. 87-95
Author(s):  
M. S. Baranava ◽  
P. A. Praskurava
Keyword(s):  
Transition Metal ◽  
Transition Metals ◽  
Exchange Interaction ◽  
Electronic Properties ◽  
Metal Atom ◽  
Transition Metal Atom ◽  
Two Dimensional ◽  
Transition Metal Atoms ◽  
Ground Magnetic ◽  
Low Dimensional

The search for fundamental physical laws which lead to stable high-temperature ferromagnetism is an urgent task. In addition to the already synthesized two-dimensional materials, there remains a wide list of possible structures, the stability of which is predicted theoretically. The article suggests the results of studying the electronic properties of MAX3 (M = Cr, Fe, A = Ge, Si, X = S, Se, Te) transition metals based compounds with nanostructured magnetism. The research was carried out using quantum mechanical simulation in specialized VASP software and calculations within the Heisenberg model. The ground magnetic states of twodimensional MAX3 and the corresponding energy band structures are determined. We found that among the systems under study, CrGeTe3 is a semiconductor nanosized ferromagnet. In addition, one is a semiconductor with a bandgap of 0.35 eV. Other materials are antiferromagnetic. The magnetic moment in MAX3 is localized on the transition metal atoms: in particular, the main one on the d-orbital of the transition metal atom (and only a small part on the p-orbital of the chalcogen). For CrGeTe3, the exchange interaction integral is calculated. The mechanisms of the formation of magnetic order was established. According to the obtained exchange interaction integrals, a strong ferromagnetic order is formed in the semiconductor plane. The distribution of the projection density of electronic states indicates hybridization between the d-orbital of the transition metal atom and the p-orbital of the chalcogen. The study revealed that the exchange interaction by the mechanism of superexchange is more probabilistic.

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