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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2020.1
- 수록면
- 209 - 220 (12page)
- DOI
- 10.7465/jkdi.2020.31.1.209
이용수
초록· 키워드
오류제보하기
Compared to clustering numerical data, clustering algorithms for categorical data have not been extensively studied, particularly for data with high-cardinality attributes. When categorical attributes have a large number of levels, clustering algorithms tend to suffer from the curse of dimensionality. In this study, we verified that a good clustering performance can be achieved in the presence of categorical attributes by com-bining clustering algorithms typically applied to numerical data with word embedding methods. Using word embedding methods that were originally developed for natural language processing, the levels of categorical attributes can be represented in a vector space, where the resulting embedding vectors would reflect the relationship between frequently appearing categories. We utilized Word2vec, GloVe, and fastText for category embedding. We also applied K-means and Gaussian mixture model for clustering the embedded data. The clustering performance of the proposed methods was compared with that of typical clustering algorithms for categorical data, namely, K-mode and robust clustering using links. In a simulation study and experiments employing real-life examples, the Gaussian mixture model with GloVe had the best performance, especially when the number of observations and complexity of data was increased.
목차
Abstract
1. Introduction
2. Backgrounds
3. Clustering with category embedding
4. Simulation experiments
5. Real data example
6. Conclusion
References
참고문헌 (45)
참고문헌 신청
Aggarwal, C. C. / 1999 / Fast algorithms for projected clustering / Proceedings of the 1999 ACM SIGMOD International Conference on Management of Data :61~72
Aggarwal, C. C. / 2000 / Finding generalized projected clusters in high dimensional spaces / Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data :70~81
Agrawal, R. / 1998 / Automatic subspace clustering of high dimensional data for data mining applications / Proceedings of the 1998 ACM-SIGMOD International Conference on Management of Data :94~105
Amiri, S. / 2018 / Clustering categorical data via ensembling dissimilarity matrices / Journal of Computational and Graphical Statistics 27:195~208
Arora, S. / 2016 / A latent variable model approach to pmi-based word embeddings / Transactions of the Association for Computational Linguistics 4:385~399
Aggarwal, C. C. / 2000 / Finding generalized projected clusters in high dimensional spaces / Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data :70~81
Agrawal, R. / 1998 / Automatic subspace clustering of high dimensional data for data mining applications / Proceedings of the 1998 ACM-SIGMOD International Conference on Management of Data :94~105
Amiri, S. / 2018 / Clustering categorical data via ensembling dissimilarity matrices / Journal of Computational and Graphical Statistics 27:195~208
Arora, S. / 2016 / A latent variable model approach to pmi-based word embeddings / Transactions of the Association for Computational Linguistics 4:385~399
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UCI(KEPA) : I410-ECN-0101-2020-041-000379850