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Automatic subspace clustering of high dimensional data for data mining applications
Open accessAbstract
Data mining applications place special requirements on clustering algorithms including: the ability to find clusters embedded in subspaces of high dimensional data, scalability, end-user comprehensibility of the results, non-presumption of any canonical data distribution, and insensitivity to the order of input records. We present CLIQUE, a clustering algorithm that satisfies each of these requirements. CLIQUE identifies dense clusters in subspaces of maximum dimensionality. It generates cluster descriptions in the form of DNF expressions that are minimized for ease of comprehension. It produces identical results irrespective of the order in which input records are presented and does not presume any specific mathematical form for data distribution. Through experiments, we show that CLIQUE efficiently finds accurate cluster in large high dimensional datasets.
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Identification of depression subtypes and relevant brain regions using a data-driven approach
Tokuda, T., Yoshimoto, J., Shimizu, Y., Okada, G., Takamura, M., Okamoto, Y., Yamawaki, S., & Doya, K. (2018) · Scientific Reports · doi:10.1038/s41598-018-32521-z · Cited by 136
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
- Agrawal, R., Gehrke, J., Gunopulos, D., & Raghavan, P.
- Venue
- ACM SIGMOD Record
- Published
- 1998-06
- Volume
- 27
- Issue
- 2
- Pages
- 94-105
- ISSN
- 0163-5808 (ISSN-L)
- OpenAlex
- W3003734944
- Cited by
- 663
- References
- 1
- FWCI
- 2.63
- Type
- journal article
- Language
- en
- Open access
- bronze
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