The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering
Abstract
As a prolific research area in data mining, subspace clustering and related problems induced a vast quantity of proposed solutions. However, many publications compare a new proposition—if at all—with one or two competitors, or even with a so-called “naïve” ad hoc solution, but fail to clarify the exact problem definition. As a consequence, even if two solutions are thoroughly compared experimentally, it will often remain unclear whether both solutions tackle the same problem or, if they do, whether they agree in certain tacit assumptions and how such assumptions may influence the outcome of an algorithm. In this survey, we try to clarify: (i) the different problem definitions related to subspace clustering in general; (ii) the specific difficulties encountered in this field of research; (iii) the varying assumptions, heuristics, and intuitions forming the basis of different approaches; and (iv) how several prominent solutions tackle different problems.
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Cites
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Fischler, M. A., & Bolles, R. C. (1981) · Communications of the ACM · doi:10.1145/358669.358692 · Cited by 25,893
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
Ankerst, M. (1999) · doi:10.1145/304181.304187 · Cited by 3,982
When Is ''Nearest Neighbor'' Meaningful?
Beyer, K., Goldstein, J., Ramakrishnan, R., & Shaft, U. (1999) · Lecture Notes in Computer Science · doi:10.1007/3-540-49257-7_15 · Cited by 2,403
High-dimensional data clustering
Bouveyron, C., Girard, S., & Schmid, C. (2007) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2007.02.009 · Cited by 382
Cited by
A Comprehensive Survey of Clustering Algorithms
Xu, D., & Tian, Y. (2015) · Annals of Data Science · doi:10.1007/s40745-015-0040-1 · Cited by 1,939
Kriegel, H., Kröger, P., Sander, J., & Zimek, A. (2011) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.30 · Cited by 900
Self-citationA survey on unsupervised outlier detection in high‐dimensional numerical data
Zimek, A., Schubert, E., & Kriegel, H. (2012) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.11161 · Cited by 864
Self-citationCopyCatch: stopping group attacks by spotting lockstep behavior in social networks
Beutel, A., Xu, W., Guruswami, V., Palow, C., & Faloutsos, C. (2013) · Proceedings of the 22nd international conference on World Wide Web · doi:10.1145/2488388.2488400 · Cited by 335
The citation advantage of linking publications to research data
Colavizza, G., Hrynaszkiewicz, I., Staden, I., Whitaker, K., & McGillivray, B. (2020) · PLOS ONE · doi:10.1371/journal.pone.0230416 · Cited by 335
Metadata
- Authors
- Kriegel, H.-P., Kröger, P., & Zimek, A.
- Venue
- ACM Transactions on Knowledge Discovery from Data
- Published
- 2009-03
- Volume
- 3
- Issue
- 1
- Pages
- 1-58
- ISSN
- 1556-4681 (ISSN-L)
- 1556-472X
- OpenAlex
- W2079361215
- Cited by
- 1,093
- References
- 6
- FWCI
- 77.35
- Type
- journal article
- Language
- en
- Open access
- closed
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- Kriegel, H.-P., Kröger, P., & Zimek, A. (2009). Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering. ACM Transactions on Knowledge Discovery from Data. https://doi.org/10.1145/1497577.1497578
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