Maximum Entropy-Based Fuzzy Clustering by Using L1-norm Space

M. GHORBANI

Makale detayları

Maximum Entropy-Based Fuzzy Clustering by Using L1-norm Space

Yazarlar:

M. GHORBANI

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Mathematics 2005 Vol.29 Issue 4, pp.431-438

Belge Tipi:

Makale

Yayın Tarihi:

3/2005

Belge Dili:

Türkçe

Document ID (Org No.):

2000748634

Sıklık:

İki Aylık

eISSN:

1303-6149

pISSN:

1300-0098

Anahtar kelimeler:

Özet:

 

One of the most important methods in analysis of large data  sets is clustering.  These methods are not only major tools to uncover  the underlying structures of a given data set, but also promising  tools to uncover local input-output relations of a complex system.   The goal of this paper is to present a new approach to fuzzy  clustering by using L1-norm space by means of a maximum entropy  inference method, where, firstly, the resulting formulas have more  beautiful form and clearer physical meaning than those obtained by  means of FCM method and secondly, the obtained criteria by this new  method are very robust.  In order to solve the cluster validity  problem and choosing the number of clusters in fuzzy clustering, we  introduce a structure strength function as clustering criterion.  With  the proposed structure strength function, we also discuss the global  minimum problem in terms of simulating methods.

 

One of the most important methods in analysis of large data  sets is clustering.  These methods are not only major tools to uncover  the underlying structures of a given data set, but also promising  tools to uncover local input-output relations of a complex system.   The goal of this paper is to present a new approach to fuzzy  clustering by using L1-norm space by means of a maximum entropy  inference method, where, firstly, the resulting formulas have more  beautiful form and clearer physical meaning than those obtained by  means of FCM method and secondly, the obtained criteria by this new  method are very robust.  In order to solve the cluster validity  problem and choosing the number of clusters in fuzzy clustering, we  introduce a structure strength function as clustering criterion.  With  the proposed structure strength function, we also discuss the global  minimum problem in terms of simulating methods.

Makale detayları

Maximum Entropy-Based Fuzzy Clustering by Using L1-norm Space

Yazarlar:

M. GHORBANI

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Mathematics 2005 Vol.29 Issue 4, pp.431-438

Belge Tipi:

Makale

Yayın Tarihi:

3/2005

Belge Dili:

Türkçe

Document ID (Org No.):

2000748634

Sıklık:

İki Aylık

eISSN:

1303-6149

pISSN:

1300-0098

Fuzzy c-means

Maximum Entropy

Structure Strength

Number of Clusters

L1-norm Space

Sayı

Makale detayları

Maximum Entropy-Based Fuzzy Clustering by Using L1-norm Space

Yazarlar: M. GHORBANI

Yayıncı: TUBITAK

Dergi: Turkish Journal of Mathematics 2005 Vol.29 Issue 4, pp.431-438

Belge Tipi: Makale

Yayın Tarihi: 3/2005

Belge Dili: Türkçe

Document ID (Org No.): 2000748634

Sıklık: İki Aylık

eISSN: 1303-6149

pISSN: 1300-0098

Fuzzy c-means

Maximum Entropy

Structure Strength

Number of Clusters

L1-norm Space

Sayı

Yazarlar:

M. GHORBANI

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Mathematics 2005 Vol.29 Issue 4, pp.431-438

Belge Tipi:

Makale

Yayın Tarihi:

3/2005

Belge Dili:

Türkçe

Document ID (Org No.):

2000748634

Sıklık:

İki Aylık

eISSN:

1303-6149

pISSN:

1300-0098