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Pattern Recognition with Fuzzy Objective Function Algorithms
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Citations
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A Comprehensive Survey of Clustering Algorithms
Dongkuan Xu,Yingjie Tian +1 more
TL;DR: This review paper begins at the definition of clustering, takes the basic elements involved in the clustering process, such as the distance or similarity measurement and evaluation indicators, into consideration, and analyzes the clustered algorithms from two perspectives, the traditional ones and the modern ones.
Journal ArticleDOI
A possibilistic fuzzy c-means clustering algorithm
TL;DR: A new model called possibilistic-fuzzy c-means (PFCM) model, which solves the noise sensitivity defect of FCM, overcomes the coincident clusters problem of PCM and eliminates the row sum constraints of FPCM.
Journal ArticleDOI
Some new indexes of cluster validity
James C. Bezdek,Nikhil R. Pal +1 more
TL;DR: This work reviews two clustering algorithms and three indexes of crisp cluster validity and shows that while Dunn's original index has operational flaws, the concept it embodies provides a rich paradigm for validation of partitions that have cloud-like clusters.
Journal ArticleDOI
Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure
Songcan Chen,Daoqiang Zhang +1 more
TL;DR: Two variants of fuzzy c-means clustering with spatial constraints, using the kernel methods, are proposed, inducing a class of robust non-Euclidean distance measures for the original data space to derive new objective functions and thus clustering theNon-E Euclidean structures in data.
Journal ArticleDOI
Prediction of protein structural classes.
Kuo-Chen Chou,Chun-Ting Zhang +1 more
TL;DR: The very high success rate for both the training- set proteins and the testing-set proteins, which has been further validated by a simulated analysis and a jackknife analysis, indicates that it is possible to predict the structural class of a protein according to its amino acid composition if an ideal and complete database can be established.
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.