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No clustering algorithm is guaranteed to find actual groups in any dataset. Thus, the selection of the most suitable clustering algorithm to be applied to a given dataset is not easy. To deal with this problem, one can apply various clustering algorithms to the dataset, generating a set of partitions (solutions). Next, one can choose the best partition generated, according to a given validation measure...
In cluster analysis, current algorithms assume that all features in the data contribute uniformly in assigning samples to clusters. This assumption can lead to poor clustering results, due to the existence of noisy and less important features. Feature weighting overcomes this issue by assigning different weights to features based on some notion of importance. According to feature weighting, more important...
When clustering produces more than one candidate to partition a finite set of objects O, there are two approaches to validation (i.e., selection of a “best” partition, and implicitly, a best value for c , which is the number of clusters in O). First, we may use an internal index, which evaluates each partition separately. Second, we may compare pairs of candidates with each other, or with a reference...
The cluster analysis deals with the problems of organization of a collection of data objects into clusters based on similarity. It is also known as the unsupervised classification of objects and has found many applications in different areas. An important component of a clustering algorithm is the distance measure which is used to find the similarity between data objects. K-means is one of the most...
Self-organizing map (SOM) has been recognized as a powerful tool in cluster analysis. This paper presents a fuzzy SOM algorithm for mixed numeric and categorical data which integrates fuzzy set theory in model exploration through a fuzzy projection instead of crisp projection. In addition, a hybrid clustering approach is proposed combining SOMs with partitive clustering algorithms for the sake of...
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