Data exploration process based on the self-organizing map

 |  Login

Show simple item record

dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Vesanto, Juha
dc.date.accessioned 2012-02-10T09:20:16Z
dc.date.available 2012-02-10T09:20:16Z
dc.date.issued 2002-05-16
dc.identifier.isbn 951-22-5897-8
dc.identifier.issn 1456-9418
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/2178
dc.description.abstract With the advances in computer technology, the amount of data that is obtained from various sources and stored in electronic media is growing at exponential rates. Data mining is a research area which answers to the challange of analysing this data in order to find useful information contained therein. The Self-Organizing Map (SOM) is one of the methods used in data mining. It quantizes the training data into a representative set of prototype vectors and maps them on a low-dimensional grid. The SOM is a prominent tool in the initial exploratory phase in data mining. The thesis consists of an introduction and ten publications. In the publications, the validity of SOM-based data exploration methods has been investigated and various enhancements to them have been proposed. In the introduction, these methods are presented as parts of the data mining process, and they are compared with other data exploration methods with similar aims. The work makes two primary contributions. Firstly, it has been shown that the SOM provides a versatile platform on top of which various data exploration methods can be efficiently constructed. New methods and measures for visualization of data, clustering, cluster characterization, and quantization have been proposed. The SOM algorithm and the proposed methods and measures have been implemented as a set of Matlab routines in the SOM Toolbox software library. Secondly, a framework for SOM-based data exploration of table-format data - both single tables and hierarchically organized tables - has been constructed. The framework divides exploratory data analysis into several sub-tasks, most notably the analysis of samples and the analysis of variables. The analysis methods are applied autonomously and their results are provided in a report describing the most important properties of the data manifold. In such a framework, the attention of the data miner can be directed more towards the actual data exploration task, rather than on the application of the analysis methods. Because of the highly iterative nature of the data exploration, the automation of routine analysis tasks can reduce the time needed by the data exploration process considerably. en
dc.format.extent 86, [103]
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher Helsinki University of Technology en
dc.publisher Teknillinen korkeakoulu fi
dc.relation.ispartofseries Acta polytechnica Scandinavica. Ma, Mathematics and computing series en
dc.relation.ispartofseries 115 en
dc.relation.haspart Juha Vesanto (1997). Using the SOM and Local Models in Time-Series Prediction. In Proceedings of Workshop on Self-Organizing Maps (WSOM'97), Espoo, Finland, pp. 209-214. [article1.pdf] © 1997 HUT. By permission.
dc.relation.haspart Esa Alhoniemi, Jaakko Hollmén, Olli Simula and Juha Vesanto (1999). Process Monitoring and Modeling Using the Self-Organizing Map. In Integrated Computer Aided Engineering Volume 6, Number 1, IOS Press, pp. 3-14. [article2.pdf] © 1999 IOS Press. By permission.
dc.relation.haspart Juha Vesanto (1999). SOM-Based Data Visualization Methods. In Intelligent Data Analysis, Volume 3, Number 2, Elsevier Science, pp. 111-126. [article3.pdf] © 1999 IOS Press. By permission.
dc.relation.haspart Esa Alhoniemi, Johan Himberg and Juha Vesanto (1999). Probabilistic Measures for Responses of Self-Organizing Map Units. In Proceeding of the International ICSC Congress on Computational Intelligence Methods and Applications (CIMA'99), ICSC Academic Press, pp. 286-290. [article4.pdf] © 1999 ICSC. By permission.
dc.relation.haspart Juha Vesanto and Jussi Ahola (1999). Hunting for Correlations in Data Using the Self-Organizing Map. In Proceeding of the International ICSC Congress on Computational Intelligence Methods and Applications (CIMA'99), ICSC Academic Press, pp. 279-285. [article5.pdf] © 1999 ICSC. By permission.
dc.relation.haspart Juha Vesanto, Johan Himberg, Esa Alhoniemi and Juha Parhankangas (1999). Self-Organizing Map in Matlab: the SOM Toolbox. In Proceedings of the Matlab DSP Conference 1999, Espoo, Finland, pp. 35-40. [article6.pdf] © 1999 Comsol Oy. By permission.
dc.relation.haspart Juha Vesanto and Esa Alhoniemi (2000). Clustering of the Self-Organizing Map. In IEEE Transactions on Neural Networks, Volume 11, Number 3, pp. 586-600. [article7.pdf] © 2000 IEEE. By permission.
dc.relation.haspart Juha Vesanto (2001). Importance of Individual Variables in the k-Means Algorithm. In Proceedings of the Pacific-Asia Conference Advances in Knowledge Discovery and Data Mining (PAKDD2001), Springer-Verlag, pp. 513-518. [article8.pdf] © 2001 Springer-Verlag. By permission.
dc.relation.haspart Markus Siponen, Juha Vesanto, Olli Simula and Petri Vasara (2001). An Approach to Automated Interpretation of SOM. In Proceedings of Workshop on Self-Organizing Map 2001 (WSOM2001), Springer, pp. 89-94. [article9.pdf] © 2001 Springer-Verlag. By permission.
dc.relation.haspart Juha Vesanto and Jaakko Hollmén (2002). An Automated Report Generation Tool for the Data Understanding Phase. In Hybrid Information Systems, edited by A. Abraham and M. Köppen, Physica Verlag, Heidelberg, pp. 611-626. [article10.pdf] © 2002 Springer-Verlag. By permission.
dc.subject.other Computer science en
dc.title Data exploration process based on the self-organizing map en
dc.type G5 Artikkeliväitöskirja fi
dc.description.version reviewed en
dc.contributor.department Department of Computer Science and Engineering en
dc.contributor.department Tietotekniikan osasto fi
dc.subject.keyword self-organizing map en
dc.subject.keyword exploratory data analysis en
dc.subject.keyword data mining en
dc.subject.keyword visualization en
dc.subject.keyword clustering en
dc.subject.keyword vector quantization en
dc.identifier.urn urn:nbn:fi:tkk-001489
dc.type.dcmitype text en
dc.type.ontasot Väitöskirja (artikkeli) fi
dc.type.ontasot Doctoral dissertation (article-based) en


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search archive


Advanced Search

article-iconSubmit a publication

Browse

My Account