Algorithms for clustering data
Algorithms for clustering data
ACM Computing Surveys (CSUR)
Digital Image Processing
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Granular computing, rough entropy and object extraction
Pattern Recognition Letters
Image segmentation evaluation: A survey of unsupervised methods
Computer Vision and Image Understanding
Handbook of Granular Computing
Handbook of Granular Computing
Rough Granular Computing in Knowledge Discovery and Data Mining
Rough Granular Computing in Knowledge Discovery and Data Mining
Standard and Fuzzy Rough Entropy Clustering Algorithms in Image Segmentation
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
Histogram thresholding using fuzzy and rough measures of association error
IEEE Transactions on Image Processing
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
Adaptive Rough Entropy Clustering Algorithms in Image Segmentation
Fundamenta Informaticae
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In data clustering there is a constant demand on development of new algorithmic schemes capable of robust and correct data handling. This demand has been additionally highly fueled and increased by emerging new technologies in data imagery area. Hierarchical clustering represents established data grouping technique with a wide spectrum of application, especially in image analysis branch. In the paper, a new algorithmic rough entropy framework has been applied in the hierarchical clustering setting. During cluster merges the quality of the resultant merges has been assessed on the base of the rough entropy. Incorporating rough entropy measure as the evaluation of cluster quality takes into account inherent uncertainty, vagueness and impreciseness. The experimental results suggest that hierarchies created during rough entropy based merging process are robust and of high quality, giving possible area for future research applications in real implementations.