Probabilistic Modeling of State Transitions on the Self-Organizing Map: Some Temporal Financial Applications

Sarlin, Peter; Yao, Zheng; Eklund, Tomas

doi:10.1109/hicss.2012.486

Cited by 3 publications

(1 citation statement)

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“…For instance, showing data variation, not design variation, and not showing data out of context relates to, and is supported by, the use of a regular grid shape. Likewise, an example of visuals being directly proportional to the quantities they represent is the adjustment of color scales used for the linked visualizations, such as normalizations of feature planes in order for all variables to be comparable (see, e.g., [25]), and the use of perceptually uniform color scales, such as CIELAB.…”

Section: A Information Visualization With the Sommentioning

confidence: 99%

Cluster Coloring of the Self-Organizing Map: An Information Visualization Perspective

Sarlin

Rönnqvist

2013

2013 17th International Conference on Information Visualisation

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This paper takes an information visualization perspective to visual representations in the general SOM paradigm. This involves viewing SOM-based visualizations through the eyes of Bertin's and Tufte's theories on data graphics. The regular grid shape of the Self-Organizing Map (SOM), while being a virtue for linking visualizations to it, restricts representation of cluster structures. From the viewpoint of information visualization, this paper provides a general, yet simple, solution to projection-based coloring of the SOM that reveals structures. First, the proposed color space is easy to construct and customize to the purpose of use, while aiming at being perceptually correct and informative through two separable dimensions. Second, the coloring method is not dependent on any specific method of projection, but is rather modular to fit any objective function suitable for the task at hand. The cluster coloring is illustrated on two datasets: the iris data, and welfare and poverty indicators.

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Section: A Information Visualization With the Sommentioning

confidence: 99%

Cluster Coloring of the Self-Organizing Map: An Information Visualization Perspective

Sarlin

Rönnqvist

2013

2013 17th International Conference on Information Visualisation

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Chance Discovery with Self-Organizing Maps: Discovering Imbalances in Financial Networks

Sarlin

2013

Studies in Computational Intelligence

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A Novel Approach for the Customer Segmentation Using Clustering Through Self-Organizing Map

Barman

Chowdhury

2019

International Journal of Business Analytics

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Customer segmentation is the process of forming smaller groups of customers according to their characteristics. Now companies can develop proper marketing strategies for each group to get the desired results. This type of direct marketing is practiced by most organizations from the size of smallest start-up to the Fortune 500 leaders. Clustering is the ideal data mining technique for customer segmentation. In this article, the authors have proposed a clustering algorithm based on the self-organizing map and minimum spanning tree for customer segmentation. The authors have used several synthetic and real-life datasets to evaluate the clustering performance of their approach. To demonstrate the effectiveness of the authors' proposed approach, they have trained few classifiers with the groups extracted from a direct marketing campaign of a Portuguese banking institution and show that the classification accuracy is better compared to the results obtained in some previous work where the full dataset has been used to train the same classifiers.

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Probabilistic Modeling of State Transitions on the Self-Organizing Map: Some Temporal Financial Applications

Cited by 3 publications

References 25 publications

Cluster Coloring of the Self-Organizing Map: An Information Visualization Perspective

Cluster Coloring of the Self-Organizing Map: An Information Visualization Perspective

Chance Discovery with Self-Organizing Maps: Discovering Imbalances in Financial Networks

A Novel Approach for the Customer Segmentation Using Clustering Through Self-Organizing Map

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