Using a diffusion-like process for clustering

O. Yadid-Pecht; M. Gur

Using a diffusion-like process for clustering

O. Yadid-Pecht, M. Gur

Research output: Contribution to conference › Paper › peer-review

Abstract

A simple clustering method using a neural net, which implements a diffusion-like, process is suggested. The implementation requires basic elements, numbered as the number of pixels, that work in parallel. The units can be viewed as simple 'neurons', requiring only a small number of local connections. In spite of its simplicity, this implementation has several advantages over commonly used fuzzy clustering methods. Specifically, it is not dependent on initial conditions and it provides the 'typicality' notion that is lacking in the well known Fuzzy C means (FCM) and its derivatives.

Original language	English
Pages	2991-2996
Number of pages	6
State	Published - 1 Dec 1994
Externally published	Yes
Event	Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) - Orlando, FL, USA Duration: 27 Jun 1994 → 29 Jun 1994

Conference

Conference	Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7)
City	Orlando, FL, USA
Period	27/06/94 → 29/06/94

ASJC Scopus subject areas

Software

Cite this

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title = "Using a diffusion-like process for clustering",

abstract = "A simple clustering method using a neural net, which implements a diffusion-like, process is suggested. The implementation requires basic elements, numbered as the number of pixels, that work in parallel. The units can be viewed as simple 'neurons', requiring only a small number of local connections. In spite of its simplicity, this implementation has several advantages over commonly used fuzzy clustering methods. Specifically, it is not dependent on initial conditions and it provides the 'typicality' notion that is lacking in the well known Fuzzy C means (FCM) and its derivatives.",

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year = "1994",

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AU - Gur, M.

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AB - A simple clustering method using a neural net, which implements a diffusion-like, process is suggested. The implementation requires basic elements, numbered as the number of pixels, that work in parallel. The units can be viewed as simple 'neurons', requiring only a small number of local connections. In spite of its simplicity, this implementation has several advantages over commonly used fuzzy clustering methods. Specifically, it is not dependent on initial conditions and it provides the 'typicality' notion that is lacking in the well known Fuzzy C means (FCM) and its derivatives.

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M3 - Paper

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T2 - Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7)

Y2 - 27 June 1994 through 29 June 1994

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Using a diffusion-like process for clustering

Abstract

Conference

ASJC Scopus subject areas

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