Mixed-type categorical and numerical data are a challenge in many applications. This general area of mixed-type data is among the frontier areas, where computational intelligence approaches are often brittle compared with the capabilities of living creatures. In this paper, unsupervised feature learning (UFL) is applied to the mixed-type data to achieve a sparse representation, which makes it easier for clustering algorithms to separate the data. Unlike other UFL methods that work with homogeneous data, such as image and video data, the presented UFL works with the mixed-type data using fuzzy adaptive resonance theory (ART). UFL with fuzzy ART (UFLA) obtains a better clustering result by removing the differences in treating categorical and numeric features. The advantages of doing this are demonstrated with several real-world data sets with ground truth, including heart disease, teaching assistant evaluation, and credit approval. The approach is also demonstrated on noisy, mixed-type petroleum industry data. UFLA is compared with several alternative methods. To the best of our knowledge, this is the first time UFL has been extended to accomplish the fusion of mixed data types.
D. Lam et al., "Clustering Data of Mixed Categorical and Numerical Type with Unsupervised Feature Learning," IEEE Access, vol. 3, pp. 1605-1613, Institute of Electrical and Electronics Engineers (IEEE), Sep 2015.
The definitive version is available at http://dx.doi.org/10.1109/ACCESS.2015.2477216
Geosciences and Geological and Petroleum Engineering
Electrical and Computer Engineering
Center for High Performance Computing Research
Keywords and Phrases
Artificial Intelligence; Computation Theory; Petroleum Industry; Virtual Reality; Clustering; Clustering Results; Fuzzy Adaptive Resonance Theories; Fuzzy ART; Mixed Type; Sparse Representation; Teaching Assistants; Unsupervised Feature Learning; Clustering Algorithms
International Standard Serial Number (ISSN)
Article - Journal
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