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Clustering Data of Mixed Categorical and Numerical Type with Unsupervised Feature Learning
IEEE Access
  • Dao Lam
  • Mingzhen Wei, Missouri University of Science and Technology
  • Donald C. Wunsch, Missouri University of Science and Technology

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.

Geosciences and Geological and Petroleum Engineering
Second Department
Electrical and Computer Engineering
Research Center/Lab(s)
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
Document Type
Article - Journal
Document Version
Final Version
File Type
© 2015 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
Publication Date
01 Sep 2015
Citation Information
Dao Lam, Mingzhen Wei and Donald C. Wunsch. "Clustering Data of Mixed Categorical and Numerical Type with Unsupervised Feature Learning" IEEE Access Vol. 3 (2015) p. 1605 - 1613 ISSN: 2169-3536
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