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Pattern Recognition with Fuzzy Objective Function Algorithms
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Citations
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Journal ArticleDOI
Fuzzy c-Means Algorithms for Very Large Data
TL;DR: This paper compares the efficacy of three different implementations of techniques aimed to extend fuzzy c-means (FCM) clustering to VL data and concludes by demonstrating the VL algorithms on a dataset with 5 billion objects and presenting a set of recommendations regarding the use of different VL FCM clustering schemes.
Journal ArticleDOI
A heuristic fuzzy logic approach to EMG pattern recognition for multifunctional prosthesis control
A.B. Ajiboye,R.Fff. Weir +1 more
TL;DR: A heuristic fuzzy logic approach to multiple electromyogram (EMG) pattern recognition for multifunctional prosthesis control that is transparent to, and easily "tweaked" by, the prosthetist/clinician is presented.
Journal ArticleDOI
Data analysis for electronic nose systems
TL;DR: This review covers aspects of analysis from data normalisation methods to pattern recognition and classification techniques, and focuses on the use of artificial intelligence techniques such as neural networks and fuzzy logic for classification and genetic algorithms for feature (sensor) selection.
Journal ArticleDOI
Uncertain Fuzzy Clustering: Interval Type-2 Fuzzy Approach to $C$ -Means
TL;DR: This paper focuses on the uncertainty associated with the fuzzifier parameter m that controls the amount of fuzziness of the final C-partition in the fuzzy C-means (FCM) algorithm.
Journal ArticleDOI
Long Term Persistence
TL;DR: In this article, the authors test Putnam's conjecture that today marked differences in social capital between the North and South of Italy were due to the culture of independence fostered by the free city-states experience in the North of Italy at the turn of the first millennium.
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.