JOURNAL ARTICLE

Analysis with Unsupervised Learning Based Techniques of Load Factor Profiles and Hyperspectral Images

Abstract

The problem of obtaining an optimal partition consistent with a series of partitions resulting from the application of various clustering algorithms is NP complete. A heuristic method based on the concepts of central partition and strong patterns developed by Edwin Diday [3] is proposed. It is presented the experience regarding the use of analysis techniques based on unsupervised learning methods of load factor profiles and hyperspectral images.

Keywords:
Hyperspectral imaging Partition (number theory) Cluster analysis Heuristic Computer science Unsupervised learning Pattern recognition (psychology) Artificial intelligence Factor (programming language) Series (stratigraphy) Spectral analysis Machine learning Data mining Mathematics Geology

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Topics

Advanced Scientific Research Methods
Life Sciences →  Agricultural and Biological Sciences →  Food Science

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