Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Touahni, R. | Sbihi, A. | Idrissi, M. Janati
Affiliations: University Ibn Tofaïl, F.S.K., LIRF, B.P.133, 14000 Kénitra, Morocco. E-mail: [email protected]
Abstract: In cluster analysis, the mode boundaries are a very important part of the hierarchy of structures that link raw data with their interpretation. The existing mode boundary detection approaches for clustering are conditioned by the adjustment of some parameters, which become critical for large dimensionality data sets. Mode boundary detection can be greatly facilitate by mapping, as a first step of process understanding, a reduction of data dimensionality. Under this assumption, an approach is discussed, based on both neural network and mathematical morphology. It requires neither a starting classification, nor an a priori number of clusters or their distribution. Data projection mapping is done using a multilayer neural network with a fast training rule based on a conjugate gradient. Mode boundaries of the underlying probability density function, estimated from the patterns in the projection space, are then easily obtained by making concepts of morphological watershed transformations suitable for their detection. The observations in the raw data space corresponding to those falling in the so-detected mode boundaries are taken as prototypes for classification. The clustering scheme, illustrated using an artificial simulation, has been applied to determine the clusters inside a set of biometrical six-dimensional data of the Guadeloupe honeybee's races.
Keywords: clustering, mode boundary detection, data projection, neural network, mathematical morphology
DOI: 10.3233/IDA-2001-5306
Journal: Intelligent Data Analysis, vol. 5, no. 3, pp. 263-282, 2001
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
[email protected]
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office [email protected]
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
如果您在出版方面需要帮助或有任何建, 件至: [email protected]