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Pattern Recognition with Fuzzy Objective Function Algorithms
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Citations
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Relational duals of the c-means clustering algorithms
TL;DR: New relational versions of the hard and fuzzy c-means algorithms are presented here for the case when the relational data can reasonably be viewed as some measure of distance.
Journal ArticleDOI
Fuzzy clustering algorithms for unsupervised change detection in remote sensing images
TL;DR: A context-sensitive technique for unsupervised change detection in multitemporal remote sensing images based on fuzzy clustering approach and takes care of spatial correlation between neighboring pixels of the difference image produced by comparing two images acquired on the same geographical area at different times.
Journal ArticleDOI
A new cluster validity measure and its application to image compression
TL;DR: This paper proposes a new validity measure that can deal with the edge degradation in vector quantisation of image compression and proposes a modified K-means algorithm that can assign more cluster centres to areas with low densities of data.
Journal ArticleDOI
High-resolution landform classification using fuzzy k -means
TL;DR: Using data from Alberta, Canada, and the French pre-Alps it is shown how these methods may easily create meaningful, spatially coherent land form classes from high resolution gridded DEMs.
Journal ArticleDOI
Damage characterization of polymer-based composite materials: Multivariable analysis and wavelet transform for clustering acoustic emission data
TL;DR: In this paper, a procedure for the investigation of local damage in composite materials based on the analysis of the signals of acoustic emission (AE) is presented, where unsupervised pattern recognition analyses (fuzzy C-means clustering) associated with a principal component analysis are used for the classification of the monitored AE events.
References
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Journal ArticleDOI
Nearest neighbor pattern classification
Thomas M. Cover,Peter E. Hart +1 more
TL;DR: The nearest neighbor decision rule assigns to an unclassified sample point the classification of the nearest of a set of previously classified points, so it may be said that half the classification information in an infinite sample set is contained in the nearest neighbor.
Book
Introduction to Statistical Pattern Recognition
TL;DR: This completely revised second edition presents an introduction to statistical pattern recognition, which is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field.
A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters
TL;DR: In this paper, two fuzzy versions of the k-means optimal, least squared error partitioning problem are formulated for finite subsets X of a general inner product space, and the extremizing solutions are shown to be fixed points of a certain operator T on the class of fuzzy, k-partitions of X, and simple iteration of T provides an algorithm which has the descent property relative to the LSE criterion function.