Book Chapter10.1007/978-1-4471-1599-1_120
Competitive Learning for Binary Valued Data
Friedrich Leisch,Andreas Weingessel,Evgenia Dimitriadou +2 more
- 02 Sep 1998
- pp 779-784
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TL;DR: A new approach for using online competitive learning on binary data, where the usual Euclidean distance is replaced by binary distance measures, which take possible asymmetries of binary data into account and therefore provide a “different point of view” for looking at the data.
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Abstract: We propose a new approach for using online competitive learning on binary data. The usual Euclidean distance is replaced by binary distance measures, which take possible asymmetries of binary data into account and therefore provide a “different point of view” for looking at the data. The method is demonstrated on two artificial examples and applied on tourist marketing research data.
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Citations
A toolbox for K-centroids cluster analysis
TL;DR: A methodological and computational framework for centroid-based partitioning cluster analysis using arbitrary distance or similarity measures is presented and a new variant of centroid neighborhood graphs is introduced which gives insight into the relationships between adjacent clusters.
274
A comparison of latent class, K-means, and K-median methods for clustering dichotomous data.
TL;DR: Simulation-based comparisons of the latent class, K-means, and K-median approaches for partitioning dichotomous data found that the 3 approaches can exhibit profound differences when applied to real data.
Binary-based similarity measures for categorical data and their application in Self- Organizing Maps
Fernando C. Lourenço,Victor Lobo,Fernando Bacao,Gestão de Informação +3 more
- 01 Jan 2004
TL;DR: Some of the most common binary-based similarity measures that can be applied to high dimensional data are reviewed and evaluated empirically using the Self-Organizing Maps (SOM) algorithm.
A novel clustering approach and prediction of optimal number of clusters: global optimum search with enhanced positioning
TL;DR: A novel clustering algorithm framework based on a variant of the Generalized Benders Decomposition, denoted as the Global Optimum Search, which can predict the optimal number of clusters, and the biological coherence of the predicted clusters is analyzed through gene ontology.
44
Clustering binary data in the presence of masking variables
TL;DR: A heuristic procedure that selects an appropriate subset from among the set of all candidate clustering variables that contribute to the definition of true cluster structure while eliminating variables that can hide that true structure.
41
References
Finding Groups in Data
Leonard Kaufman,Peter J. Rousseeuw +1 more
- 01 Jan 1990
TL;DR: In this article, an electrical signal transmission system for railway locomotives and rolling stock is proposed, where a basic pulse train is transmitted whereof the pulses are of a selected first amplitude and represent a train axle count, and a spike pulse of greater selected amplitude is transmitted, occurring immediately after the axle count pulse to which it relates, whenever an overheated axle box is detected.
10.1K
•Book
Pattern recognition and neural networks
Brian D. Ripley,N. L. Hjort +1 more
- 01 Jan 1996
TL;DR: Professor Ripley brings together two crucial ideas in pattern recognition; statistical methods and machine learning via neural networks in this self-contained account.
6.4K
Pattern Recognition and Neural Networks
Yann LeCun,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio +3 more
- 01 Jan 1995
TL;DR: Title Type pattern recognition with neural networks in c++ PDF pattern recognition and neural networks PDF Neural networks for pattern recognition advanced texts in econometrics PDF neural networks for applied sciences and engineering from fundamentals to complex pattern recognition PDF
3.7K
A comparison of several cluster algorithms on artificial binary data [Part 1]. Scenarios from travel market segmentation [Part 2: Working Paper 19].
Sara Dolnicar,Friedrich Leisch,Andreas Weingessel,Christian Buchta,Evgenia Dimitriadou +4 more
- 01 Jan 1998
TL;DR: The power and stability of several popular clustering algorithms under the condition that the correct number of clusters is known are concentrated on.
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