Dasarathy, BV (1975) An innovative clustering technique for unsupervized learning in the context of remotely sensed earth resources data analysis. In: International Journal of Systems Science, 6 (1). pp. 23-32.
Full text not available from this repository. (Request a copy)Abstract
A new clustering technique, based on the concept of immediato neighbourhood, with a novel capability to self-learn the number of clusters expected in the unsupervized environment, has been developed. The method compares favourably with other clustering schemes based on distance measures, both in terms of conceptual innovations and computational economy. Test implementation of the scheme using C-l flight line training sample data in a simulated unsupervized mode has brought out the efficacy of the technique. The technique can easily be implemented as a front end to established pattern classification systems with supervized learning capabilities to derive unified learning systems capable of operating in both supervized and unsupervized environments. This makes the technique an attractive proposition in the context of remotely sensed earth resources data analysis wherein it is essential to have such a unified learning system capability.
| Item Type: | Journal Article |
|---|---|
| Publication: | International Journal of Systems Science |
| Publisher: | Taylor and Francis Group |
| Additional Information: | Copy right of this article belongs to Taylor and Francis Group. |
| Department/Centre: | Division of Electrical Sciences |
| Date Deposited: | 09 Jan 2010 05:44 |
| Last Modified: | 05 Mar 2019 06:19 |
| URI: | http://eprints.iisc.ac.in/id/eprint/24250 |
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