presenter : jhou, yu-liang authors : yiu-ming cheung, hong jia 2013,pr
DESCRIPTION
Categorical-and-numerical-attribute data clustering based on a unified similarity metric without knowing cluster number. Presenter : JHOU, YU-LIANG Authors : Yiu-ming Cheung, Hong Jia 2013,PR. Outlines. Motivation Objectives Methodology Experiments Conclusions Comments. - PowerPoint PPT PresentationTRANSCRIPT
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Intelligent Database Systems Lab
Presenter : JHOU, YU-LIANG
Authors : Yiu-ming Cheung, Hong Jia
2013,PR
Categorical-and-numerical-attribute data clustering based on a unified
similarity metric without knowing cluster number
![Page 2: Presenter : JHOU, YU-LIANG Authors : Yiu-ming Cheung, Hong Jia 2013,PR](https://reader035.vdocuments.net/reader035/viewer/2022062816/568156be550346895dc4549b/html5/thumbnails/2.jpg)
Intelligent Database Systems Lab
OutlinesMotivationObjectivesMethodologyExperimentsConclusionsComments
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Intelligent Database Systems Lab
Motivation• It is a nontrivial task to perform clustering on
mixed data because there exists an awkward
gap between the similarity metrics for
categorical and numerical data.
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Objectives• This paper presents a general clustering framework
based on the concept of object-cluster similarity and
gives a unified similarity metric which can be applied
to the data with categorical, numerical, and mixed
attributes.
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Methodologyobject-cluster similarity metric
categorical attribute
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Intelligent Database Systems Lab
Methodologyobject-cluster similarity metric
• numerical attributes
• mixed data
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MethodologyIterative clustering algorithm
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MethodologyAutomatic selection of cluster number
Competition mechanism
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Intelligent Database Systems Lab
MethodologyAutomatic selection of cluster number
Penalized mechanism
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Experiments-data sets
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Experiments mixed data
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Experiments categorical data
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Conclusions• We adopt our new approach can improve the time-
consuming and efficiency of the process and
overcome the cluster number selection problem.
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Intelligent Database Systems Lab
Comments• Advantages More save time and efficiency .Applications-Clustering