Managing complexity in large data bases using self-organizing maps

Back, Barbro; Irjala, Mikko; Sere, Kaisa; Vanharanta, Hannu

doi:10.1016/s0959-8022(98)00009-5

Cited by 58 publications

(33 citation statements)

References 9 publications

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“…More the income, higher is the demand for money (Levich, 1998 (Back et al, 1998;Jardin et al, 2011). The process starts with identifying which data to consider in the data warehouse and then preprocessing these data to be ready for analysis.…”

Section: Combine Both Modelsmentioning

confidence: 99%

“…Based on a computing model similar to the underlying structure of the brain, ANNs share the brain's ability to learn or adapt in response to external inputs. When exposed to a stream of training data, ANNs can discover previously unknown relationships and learn complex nonlinear mappings in the data (Back et al, 1998;Jardin et al, 2011). numerous mapping functions so that the desired output can be produced using a given set of inputs (Kim, 1999;Kim et al, 1999).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

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The Prediction of Currency Crises through Artificial Neural Network

Lee¹,

Park²

2016

Journal of Intelligence and Information Systems

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․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․This study examines the causes of the Asian exchange rate crisis and compares it to the European Monetary System crisis. In 1997, emerging countries in Asia experienced financial crises. Previously in 1992, currencies in the European Monetary System had undergone the same experience. This was followed by Mexico in 1994. The objective of this paper lies in the generation of useful insights from these crises. This research presents a comparison of South Korea, United Kingdom and Mexico, and then compares three different models for prediction.Previous studies of economic crisis focused largely on the manual construction of causal models using linear techniques. However, the weakness of such models stems from the prevalence of nonlinear factors in reality. This paper uses a structural equation model to analyze the causes, followed by a neural network model to circumvent the linear model's weaknesses. The models are examined in the context of predicting exchange ratesIn this paper, data were quarterly ones, and Consumer Price Index, Gross Domestic Product, Interest Rate, Stock Index, Current Account, Foreign Reserves were independent variables for the prediction. However, time periods of each country's data are different.Lisrel is an emerging method and as such requires a fresh approach to financial crisis prediction model design, along with the flexibility to accommodate unexpected change. This paper indicates the neural network model has the greater prediction performance in Korea, Mexico, and United Kingdom. However, in Korea, the multiple regression shows the better performance. In Mexico, the multiple regression is almost indifferent to the Lisrel. Although Lisrel doesn't show the significant performance, the refined model is expected to show the better result. The structural model in this paper should contain the psychological factor and other invisible areas in the future work. The reason of the low hit ratio is that the alternative model in this paper uses only the financial market data. Thus, we cannot consider the other important part. Korea's hit ratio is lower than that of United Kingdom. So, there must be the other construct that affects the financial market. So does Mexico. However, the United Kingdom's financial market is more influenced and explained by the financial factors than Korea and Mexico.

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Section: Combine Both Modelsmentioning

confidence: 99%

Section: Artificial Neural Networkmentioning

confidence: 99%

The Prediction of Currency Crises through Artificial Neural Network

Lee¹,

Park²

2016

Journal of Intelligence and Information Systems

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“…It is capable of providing an easily interpretable non-linear description of the multidimensional data distribution on a two-dimensional plane without losing sight of individual indicators. The two-dimensional output of the SOM makes it particularly useful for static visualizations, or summarizations, of large amounts of information (Back et al, 1998).…”

Section: Ecb Working Paper Series No 1382mentioning

confidence: 99%

Mapping the state of financial stability

Sarlin

Peltonen

2013

Journal of International Financial Markets, Institutions and Mo

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in Wo r k i n g Pa Pe r S e r i e S n o 1382 / S e P t e m b e r Macroprudential Research NetworkThis paper presents research conducted within the Macroprudential Research Network (MaRs). The network is composed of economists from the European System of Central Banks (ESCB), i.e. the 27 national central banks of the European Union (EU) and the European Central Bank. The objective of MaRs is to develop core conceptual frameworks, models and/or tools supporting macro-prudential supervision in the EU.The research is carried out in three work streams: 1) Macro-financial models linking financial stability and the performance of the economy; 2) Early warning systems and systemic risk indicators; 3) Assessing contagion risks. The refereeing process of this paper has been coordinated by a team composed of Cornelia Holthausen, Kalin Nikolov and Bernd Schwaab (all ECB).The paper is released in order to make the research of MaRs generally available, in preliminary form, to encourage comments and suggestions prior to final publication. The views expressed in the paper are the ones of the author(s) and do not necessarily reflect those of the ECB or of the ESCB. ECB Working Paper Series No 1382September 2011 Abstract 4 Non-technical summary 5

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“…For example, the SOM has been used for financial benchmarking (Back et al 1998;Eklund et al 2003), macro-economic analysis (Kaski and Kohonen 1996;Länsiluoto 2007) , and bankruptcy prediction (Back et al 1995;Kiviluoto 1998;Martín-del-Brío and Serrano-Cinca 1993) . However, regardless of its obvious benefits, the SOM has not been widely applied in customer segmentation tasks.…”

Section: The Sommentioning

confidence: 99%

Customer portfolio analysis using the SOM

Holmbom

Eklund

Back

2011

IJBIS

Self Cite

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In order to compete for profitable customers, companies are looking to add value using Customer Relationship Management (CRM). One subset of CRM is customer segmentation, which is the process of dividing customers into groups based upon common features or needs. Segmentation methods can be used for customer portfolio analysis (CPA), the process of analyzing the profitability of customers. This study was made for a case organization, who wanted to identify their profitable and unprofitable customers, in order to gain knowledge on how to develop their marketing strategies.Data about the customers were gathered from the case organization's own database. The Self-Organizing Map (SOM) was used to divide the customers into segments, which were then analyzed in light of product sales information.

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Managing complexity in large data bases using self-organizing maps

Cited by 58 publications

References 9 publications

The Prediction of Currency Crises through Artificial Neural Network

The Prediction of Currency Crises through Artificial Neural Network

Mapping the state of financial stability

Customer portfolio analysis using the SOM

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