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Entropy-based subspace clustering for mining numerical data
Abstract
Mining numerical data is a relatively difficult problem in data mining. Clustering is one of the techniques. We consider a database with numerical attributes, in which each transaction is viewed as a multi-dimensional vector. By studying the clusters formed by these vectors, we can discover certain behaviors hidden in the data. Traditional clustering algorithms find clusters in the full space of the data sets. This results in high dimensional clusters, which are poorly comprehensible to human. One important task in this setting is the ability to discover clusters embedded in the subspaces of a high-dimensional data set. This problem is known as subspace clustering. We follow the basic assumptions of previous work CLIQUE. It is found that the number of subspaces with clustering is very large, and a criterion called the coverage is proposed in CLIQUE for the pruning. In addition to coverage, we identify new useful criteria for this problem and propose an entropybased algorithm called ENC...
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Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
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Liu, S., Maljovec, D., Wang, B., Bremer, P.-T., & Pascucci, V. (2017) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2016.2640960 · Cited by 427
The 2017 IEEE International Conference on Fuzzy Systems
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Hybrid entity clustering using crowds and data
Lee, J., Cho, H., Park, J.-W., Cha, Y.-R., Hwang, S.-W., Nie, Z., & Wen, J.-R. (2013) · The VLDB Journal · doi:10.1007/s00778-013-0328-8 · Cited by 15
Metadata
- Authors
- Cheng, C.-H., Fu, A. W., & Zhang, Y.
- Venue
- Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 1999-08
- Pages
- 84-93
- OpenAlex
- W2042035594
- Cited by
- 543
- References
- 1
- FWCI
- 4.53
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Entropy-based subspace clustering for mining numerical data. https://app.cochat.ai/papers/10.1145/312129.312199
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- Cheng, C.-H., Fu, A. W., & Zhang, Y. (1999). Entropy-based subspace clustering for mining numerical data. Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining. https://doi.org/10.1145/312129.312199
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