Six methods are used for clustering data containing two different objects: sugar-beet plants
and weed. These objects are described by 19 different features, i.e. shape and color features.
There is also information about the distance between sugar-beet plants that is used for
labeling clusters. The methods that are evaluated: k-means, k-medoids, hierarchical clustering,
competitive learning, self-organizing maps and fuzzy c-means. After using the methods on
plant data, clusters are formed. The clusters are labeled with three different proposed
methods: expert, database and context method. Expert method is using a human for giving
initial cluster centers that are labeled. The database method is using a database as an expert
that provides initial cluster centers. The context method is using information about the
environment, which is the distance between sugar-beet plants, for labeling the clusters.
The algorithms that were tested, with the lowest achieved corresponding error, are: k-means
(3.3%), k-medoids (3.8%), hierarchical clustering (5.3%), competitive learning (6.8%), self-
organizing maps (4.9%) and fuzzy c-means (7.9%). Three different datasets were used and the
lowest error on dataset0 is 3.3%, compared to supervised learning methods where it is 3%.
For dataset1 the error is 18.7% and for dataset2 it is 5.8%. Compared to supervised methods,
the error on dataset1 is 11% and for dataset2 it is 5.1%. The high error rate on dataset1 is due
to the samples are not very well separated in different clusters. The features from dataset1 are
extracted from lower resolution on images than the other datasets, and another difference
between the datasets are the sugar-beet plants that are in different growth stages.
The performance of the three methods for labeling clusters is: expert method (6.8% as the
lowest error achieved), database method (3.7%) and context method (6.8%). These results
show the clustering results by competitive learning where the real error is 6.8%.
Unsupervised-learning methods for clustering can very wel...
