<p>Six methods are used for clustering data containing two different objects: sugar-beet plants </p><p>and weed. These objects are described by 19 different features, i.e. shape and color features. </p><p>There is also information about the distance between sugar-beet plants that is used for </p><p>labeling clusters. The methods that are evaluated: k-means, k-medoids, hierarchical clustering, </p><p>competitive learning, self-organizing maps and fuzzy c-means. After using the methods on </p><p>plant data, clusters are formed. The clusters are labeled with three different proposed </p><p>methods: expert, database and context method. Expert method is using a human for giving </p><p>initial cluster centers that are labeled. The database method is using a database as an expert </p><p>that provides initial cluster centers. The context method is using information about the </p><p>environment, which is the distance between sugar-beet plants, for labeling the clusters. </p><p> </p><p>The algorithms that were tested, with the lowest achieved corresponding error, are: k-means </p><p>(3.3%), k-medoids (3.8%), hierarchical clustering (5.3%), competitive learning (6.8%), self- </p><p>organizing maps (4.9%) and fuzzy c-means (7.9%). Three different datasets were used and the </p><p>lowest error on dataset0 is 3.3%, compared to supervised learning methods where it is 3%. </p><p>For dataset1 the error is 18.7% and for dataset2 it is 5.8%. Compared to supervised methods, </p><p>the error on dataset1 is 11% and for dataset2 it is 5.1%. The high error rate on dataset1 is due </p><p>to the samples are not very well separated in different clusters. The features from dataset1 are </p><p>extracted from lower resolution on images than the other datasets, and another difference </p><p>between the datasets are the sugar-beet plants that are in different growth stages. </p><p> </p><p>The performance of the three methods for labeling clusters is: expert method (6.8% as the </p><p>lowest error achieved), database method (3.7%) and context method (6.8%). These results </p><p>show the clustering results by competitive learning where the real error is 6.8%. </p><p> </p><p>Unsupervised-learning methods for clustering can very well be used for plant identification. </p><p>Because the samples are not classified, an automatic labeling technique must be used if plants </p><p>are to be identified. The three proposed techniques can be used for automatic labeling of </p><p>plants.</p>
| Identifer | oai:union.ndltd.org:UPSALLA/oai:DiVA.org:hh-247 |
| Date | January 2006 |
| Creators | Jelacic, Mersad |
| Publisher | Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Högskolan i Halmstad/Sektionen för Informationsvetenskap, Data- och Elektroteknik (IDE) |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, text |
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