<b>A Study of Feature Clustering Analysis based on the Hidden Layer Representation of an Autoencoder</b>

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Description

Analysis of customer purchase behavior is an active research area. Several recent studies in this area have focused on detecting groups based on similar features from the available business data. One of the efficient approaches used for dividing all data into several groups with similar features is called clustering. Application of clustering to customer or item data can generate results that can help a company develop efficient marketing strategies. However, it is a non-trivial task to apply a general clustering method to a dataset with high dimensionality and sparsity owing to the difficulty of defining an appropriate similarity metric between the data samples. In this situation, one should reduce the dimensionality of data in advance. The autoencoder is a well-known model based on neural network, which can convert high-dimensional nonlinear data to low-dimensional expressions. In this research, we propose a method for clustering high-dimensional sparse data efficiently with deep learning method. The first step of our proposed method is to adopt SDAE for dimensionality reduction. The second step is to conduct a clustering analysis using the hidden layer expressions of SADE. The effectiveness and the usability of the proposed method are clarified through its application on real purchase history data.

Journal

  • Total Quality Science

    Total Quality Science 5 (1), 11-22, 2019-09-30

    The Japanese Society for Quality Control

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