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2016 Fiscal Year Final Research Report

The quantile method for symbolic data analysis.

Research Project

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Project/Area Number 25330268
Research Category

Grant-in-Aid for Scientific Research (C)

Allocation TypeMulti-year Fund
Section一般
Research Field Intelligent informatics
Research InstitutionTokyo Denki University

Principal Investigator

Ichino Manabu  東京電機大学, 理工学部, 名誉教授 (40057245)

Research Collaborator BRITO Paula  Faculty of Economics of the University of Porto, Associate Professor
UMBLEJA Kadri  Tallinn University of Technology, Department of Computer Control
Project Period (FY) 2013-04-01 – 2017-03-31
Keywordssymbolic data analysis / data mining / Cartesian system model / quantile method / PCA / hierarchical clustering / visualization / regression model
Outline of Final Research Achievements

We often use intervals and histograms to summarize the given large numbers of numerical data sets. We use the term symbolic data to call such summarized data and data by the aggregation of different data tables.The quantile method transforms the given symbolic data table to a different sized numerical data table by a unified way. Then, we realize various data analysis methods on the transformed data. This research report includes three quantile methods for symbolic data: (1) A hierarchical method of conceptual clustering; (2) Visualization of multidimensional symbolic data; and (3) The lookup table regression model.

Free Research Field

知能情報学

URL: 

Published: 2018-03-22  

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