Competitive Learning for Binary Valued Data

  title={Competitive Learning for Binary Valued Data},
  author={Friedrich Leisch and Andreas Weingessel and Evgenia Dimitriadou},
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. 
Nearest Neighbor Median Shift Clustering for Binary Data
The theory and practice behind a new modal clustering method for binary data based on the nearest neighbor median shift, which can discover accurately the location of clusters in binary data with theoretical and experimental analyses is described.
A toolbox for K
  • F. Leisch
  • Computer Science
    Comput. Stat. Data Anal.
  • 2006
Bagged Clustering
|A new ensemble method for cluster analysis is introduced, which can be interpreted in two di erent ways: As complexity-reducing preprocessing stage for hierarchical clustering and as combination
Categorical Topological Map
This paper proposes a probabilistic formalism where the neurons now represent probability tables in topological maps, and shows the good quality of the topological order obtained as well as its performances in classification.
Artificial binary data scenarios
This manual describes artificial binary data scenarios. These data sets can be used to compare the performance of algorithms for market segmentation. The data sets described in this manual are
Binary-based similarity measures for categorical data and their application in Self- Organizing Maps
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.
Probabilistic Topological Map and Binary data
This paper adapts the Bernoulli mixture approach to the model of binary topological map and shows that using a probabilistic formalism gives rise to better quantization process and classification performances.
A Comparison of Latent Class, K-Means, and K-Median Methods for Clustering Dichotomous Data
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.
Relational topological clustering
An hybrid algorithm is proposed, which deals linearly with large datasets, provides a natural clusters identification and allows a visualization of the clustering result on a two dimensional grid while preserving the a priori topological order of the data.
A comparison of several cluster algorithms on artificial binary data [Part 2]. Scenarios from travel market segmentation. Part 2 (Addition to Working Paper No. 7).
This work is an addition to SFB Working Paper No. 7 where hard competitive learning (HCL), neural gas (NGAS), k-means and self organizing maps (SOMs) were compared and the results of five additional algorithms are evaluated.


A comparison of several cluster algorithms on artificial binary data [Part 1]. Scenarios from travel market segmentation [Part 2: Working Paper 19].
The power and stability of several popular clustering algorithms under the condition that the correct number of clusters is known are concentrated on.
Finding Groups in Data: An Introduction to Chster Analysis
This book make understandable the cluster analysis is based notion of starsmodern treatment, which efficiently finds accurate clusters in data and discusses various types of study the user set explicitly but also proposes another.
Pattern Recognition and Neural Networks
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
Cluster Analysis for Applications
Some competitive learning methods
  • Some competitive learning methods
  • 1997
Some competitive learning methods.
  • 1997