Superparamagnetic clustering of data — The definitive solution of an ill-posed problem

Eytan Domany

Physica A: Statistical Mechanics and its Applications, 1999, vol. 263, issue 1, 158-169

Abstract: Clustering is an important technique in exploratory data analysis, with applications in image processing object classification, target recognition, data mining etc. The aims to partition data according to natural classes present in it, assigning data points that are “more similar” to the same “cluster”. We solved this ill-posed problem without making any assumptions about the structure of the data, by using a physical system at an analog computer. The physical system we use is a disordered (granular) magnet. The method was tested successfully on a variety of artificial and real-life problems, such as classification of flowers, processing of satellite images, speech recognition and identification of textures and images. We are currently involved in several collaborations, applying the method to image classification, fMRI data analysis and classification of protein structures.

Date: 1999
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437198004944
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:263:y:1999:i:1:p:158-169

DOI: 10.1016/S0378-4371(98)00494-4

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().