Advisor : Dr. Hsu Presenter : Wen-Hsiang Hu Authors : D.S. Guru*; Bapu B. Kiranagi;

Intelligent Database Systems Lab

國立雲林科技大學National Yunlin University of Science and Technology

Multivalued type dissimilarity measure and concept of mutual dissimilarity value for clustering symb

olic patternsAdvisor : Dr. Hsu

Presenter : Wen-Hsiang Hu

Authors : D.S. Guru*;

Bapu B. Kiranagi;

Pattern Recognition Society. Published by Elsevier Ltd, 2004, Pages:151 - 156

2Intelligent Database Systems Lab

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Outline Motivation Objective Introduction Dissimilarity Measure Modified Agglomerative clustering technique by

introducing the concept of MDV Experiments Conclusion Personal Opinion


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Motivation For methods [2-5], the degree of proximity between two symb

olic patterns is assumed to be crisp and symmetric.

However, the proximity can, in general, be expected either to lie within a certain range or to be an instance of multivalued type in addition to being non-symmetric.

A special instance of crisp, non-symmetric and Boolean.


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Objective In order to work on such unconventional proximity value.


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Introduction We present a novel dissimilarity measure to estimate the degre

e of dissimilarity between two symbolic patterns. The proposed measure unlike other methods [2–5] approximat

es degree of dissimilarity by multivalued type data and in addition, it is non-symmetric.

[2]

[5]

[3][4]


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A novel dissimilarity measure Let Fi and Fj be two symbolic patterns described by n interval

valued features as follows:

For example:


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A novel dissimilarity measure (cont.)

The degree of dissimilarity of Fi to Fj , with respect to the kth feature (irrespective of overlapping or no overlapping) is characterized by

Fik Fjk


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The “Mutual dissimilarity value” between two patterns is defined to be the magnitude of the vector, which is the sum of the scalar times of the vectors representing the degree of dissimilarity between the patterns. i.e.

MDV is derived by the use of non-symmetric values Di→j and Dj→i with different weight factors α and β.

If Di→j and Dj→i are one and the same (as in conventional techniques), the weight factors do not convey any meaning.

where α and β are scalars (weights).


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Initially m clusters


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For sake of simplicity the weight factors α and β are set to 1.

Experiments

[2]

[5]

[3][4]

[5][2][3][4]


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Experiments (cont.)


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In this paper, a novel dissimilarity measure and a modified clustering algorithm by introducing the concept of MDV is proposed for clustering symbolic patterns.

The proposed method is experimentally validated for its efficacy and is shown to have high consistency with human perception.

Conclusion


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Our method bears the following characteristics: It is simple and computationally efficient. It can be employed on quantitative, qualitative and

multivalued qualitative symbolic data types. It is non-parametric. Being based on MDV it works on multivalued type

proximity matrix.

Conclusion (cont.)


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Drawback Does not explain what is suitable weight factors （ αand

β） for obtaining a better cluster of symbolic patterns

Application Apply dissimilarity to ART

Personal Opinion

Advisor : Dr. Hsu Presenter : Wen-Hsiang Hu Authors : D.S. Guru*; Bapu B. Kiranagi;

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