Extracting Salient Dimensions for Automatic SOM Labeling

Azcarraga, Arnulfo P.; Hsieh, Ming-Che; Pan, Shan L.; Setiono, Rudy

doi:10.1109/tsmcc.2004.843177

Cited by 18 publications

(4 citation statements)

References 24 publications

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“…For the bank industry it is essential to understand which features are most important for the clustering results. To investigate this, we adopt the salient dimension methodology presented in [3] and explained in the Appendix B. This approach identifies features whose values are statistically significant in different clusters, and are called salient dimensions.…”

Section: Cluster Interpretationmentioning

confidence: 99%

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Learning Latent Representations of Bank Customers With The Variational Autoencoder

Mancisidor¹,

Kampffmeyer²,

Aas³

et al. 2019

Preprint

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Learning data representations that reflect the customers' creditworthiness can improve marketing campaigns, customer relationship management, data and process management or the credit risk assessment in retail banks. In this research, we adopt the Variational Autoencoder (VAE), which has the ability to learn latent representations that contain useful information. We show that it is possible to steer the latent representations in the latent space of the VAE using the Weight of Evidence and forming a specific grouping of the data that reflects the customers' creditworthiness. Our proposed method learns a latent representation of the data, which shows a well-defied clustering structure capturing the customers' creditworthiness. These clusters are well suited for the aforementioned banks' activities. Further, our methodology generalizes to new customers, captures high-dimensional and complex financial data, and scales to large data sets.

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Section: Cluster Interpretationmentioning

confidence: 99%

“…It is possible to specify other distributions for p(•) and q(•). However, Gaussian distributions are appropriate for our data sets, and we assume a diagonal covariance matrix as in the original VAE 3. The performance period is the time interval in which if customers are at any moment 90+dpd, then their ground truth class is y = 1.…”

mentioning

confidence: 99%

Learning Latent Representations of Bank Customers With The Variational Autoencoder

Mancisidor¹,

Kampffmeyer²,

Aas³

et al. 2019

Preprint

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show abstract

“…Interpreting complex data by visual inspection is something that has proven its value in marketing research. (Seret, Verbraken, Versailles, & Baesens, 2012;Azcarraga, Hsieh, Pan, & Setiono, 2005) present a methodology based on selforganising maps (SOM) that allows marketeers to get a 2D visual representation of their whole user base.…”

Section: Related Workmentioning

confidence: 99%

Understanding Recommendation Quality Using Embeddings

et al. 2017

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Current methods of evaluating the quality of recommender systems are based on averages of metrics such as the average normalized discounted cumulative gain, average diversity and average reciprocity. Averages of metrics give a good sense of the overall quality of the recommendations, but not of how their quality is distributed with respect to the recommendation system's users or items. This paper presents a visual method, based on embedding a high dimensional content feature-space into a 2D image, that is capable of providing insights in which users are receiving high quality recommendations and how biased recommendation quality is with respect to different types of users. Through a proof of concept in the domain of job recommendation we show that our method allows business people to come to relevant answers to the question "For which of my users does my recommender system work well/poorly?".

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“…Other researchers [9,10] propose a method in which human editors are presented with a list of terms or candidate labels in order to provide the base for devising a label covering the central aspects of every category. If we give an expert in the dominion of the collection an adequate description of the categories, based upon the most relevant terms that characterize them, results can be quite good.…”

Section: Representing Categories By User-edited Labelsmentioning

confidence: 99%

Category labelling for automatic classification scheme generation

Jiménez

2007

Electronic Workshops in Computing

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This paper proposes a research line for developing new ways of automatically characterizing groups of documents, being them either clusters or categories. This research line is based upon the works of many other researchers and tries to summarize the most problematic issues of category labelling in order to devise possible solutions. Various lines of action are described, as well as future research lines and developments.

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Extracting Salient Dimensions for Automatic SOM Labeling

Cited by 18 publications

References 24 publications

Learning Latent Representations of Bank Customers With The Variational Autoencoder

Learning Latent Representations of Bank Customers With The Variational Autoencoder

Understanding Recommendation Quality Using Embeddings

Category labelling for automatic classification scheme generation

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