search query: @keyword association rules / total: 3
reference: 2 / 3
Author: | Lundqvist, Leo |
Title: | Deriving a Rule Set from a Large Set of Data |
Härledning av ett regelverk ur en stor datamängd | |
Säännöstön johtaminen suuresta tietomäärästä | |
Publication type: | Master's thesis |
Publication year: | 2006 |
Pages: | 37 Language: eng |
Department/School: | Teknillisen fysiikan ja matematiikan osasto |
Main subject: | Informaatiotekniikka (T-115) |
Supervisor: | Simula, Olli |
Instructor: | Silvola, Risto |
OEVS: | Electronic archive copy is available via Aalto Thesis Database.
Instructions Reading digital theses in the closed network of the Aalto University Harald Herlin Learning CentreIn the closed network of Learning Centre you can read digital and digitized theses not available in the open network. The Learning Centre contact details and opening hours: https://learningcentre.aalto.fi/en/harald-herlin-learning-centre/ You can read theses on the Learning Centre customer computers, which are available on all floors.
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Location: | P1 Ark TF80 | Archive |
Keywords: | self-organizing map clustering association rules product data itseorganisoiva kartta klusterointi assosiaatio säännöt tuotedata själv-organiserande karta kluster associations regler produkt data |
Abstract (eng): | The acquisition of correct data is of great importance for all data mining tasks. Data errors in product data can be very costly for a company and improving the data quality is therefore of high importance. By making the acquisition process more efficient a possible bottleneck in the product management can also be removed. In this work methods for finding rules and correlations from the data are presented. Special emphasis is placed on methods capable of handling large amounts of data and on pre processing the data to make it more easily handled. Clustering is used to divide the data into smaller data sets which can be handled more efficiently than the whole data. This also makes it possible to better find local patterns in the data. The clustering is implemented using self-organizing maps. To find rules in the data set both correlation analysis and association rules are used. Both methods can be used both globally on the whole data set and locally on the data clusters. The methods presented are then applied to a product data set provided by Nokia Networks. Here the goal is to predict data needed for an Enterprise Resource Planning system using data from a Product Data Management system. |
ED: | 2006-09-28 |
INSSI record number: 32425
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