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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...
Clustering is a widely used to discover underlying patterns and groups in data and there is a need to validate the quality of clusters generated by the numerous clustering algorithms in use. The need for cluster validitation arises from the fundamental definition of unsupervised learning. As clustering is an unsupervised learning process, the prediction of correct number of clusters is a hurdle which...
Cluster validation is an important issue in cluster analysis. In this paper, a comparative study on validity criteria is performed with linear fuzzy clustering that can be identified with a local PCA technique. Besides the standard fuzzification approach, the entropy regularization approach is responsible for fuzzification of data partition and the approach implies a close relation between FCM-type...
As an important issue in cluster analysis, cluster validation is the process of evaluating performance of clustering algorithms under varying input conditions. Many existing methods address clustering results of low-dimensional data. This paper presents new solution to the problem of cluster validation for subspace clustering on high dimensional data. We first propose two new measurements for the...
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