2018
DOI: 10.1007/978-3-319-77404-6_2
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Approximate Correlation Clustering Using Same-Cluster Queries

Abstract: Ashtiani et al. (NIPS 2016) introduced a semi-supervised framework for clustering (SSAC) where a learner is allowed to make samecluster queries. More specifically, in their model, there is a query oracle that answers queries of the form "given any two vertices, do they belong to the same optimal cluster?". In many clustering contexts, this kind of oracle queries are feasible. Ashtiani et al. showed the usefulness of such a query framework by giving a polynomial time algorithm for the k-means clustering proble… Show more

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Cited by 21 publications
(64 citation statements)
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“…Active Learning with Enriched Queries: Our work also fits into a long line of recent studies on learning with natural enriched queries. As previously mentioned, Angluin's [19] original membership query model can be viewed in this vein, and many types of specific enriched queries such as comparisons [25,4,5,6,3,8,7,26,27,9,28], cluster-queries [29,30,31,32,33,34,35,36,37], mistake queries [38], separation queries [39], and more have been studied since. Our work relies most closely on the general framework for active learning with enriched queries introduced by KLMZ [3] in 2017, which we discuss in greater depth in Section 2.5.…”
Section: Related Workmentioning
confidence: 99%
“…Active Learning with Enriched Queries: Our work also fits into a long line of recent studies on learning with natural enriched queries. As previously mentioned, Angluin's [19] original membership query model can be viewed in this vein, and many types of specific enriched queries such as comparisons [25,4,5,6,3,8,7,26,27,9,28], cluster-queries [29,30,31,32,33,34,35,36,37], mistake queries [38], separation queries [39], and more have been studied since. Our work relies most closely on the general framework for active learning with enriched queries introduced by KLMZ [3] in 2017, which we discuss in greater depth in Section 2.5.…”
Section: Related Workmentioning
confidence: 99%
“…[24] considers k-means and provide an algorithm using O(k 2 log k + k log n) same cluster queries, while Ref. [25] uses O( k 14 log k log n 6 ) same-cluster queries for computing (1 + )-approximate correlation clustering; Ref. [26] provides an exact algorithm using 2C OPT same-cluster queries, where C OPT denotes the number of "disagreements" in the optimal solution.…”
Section: Techniques and Relation To Prior Workmentioning
confidence: 99%
“…Related to the quality of clustering results is the quality of the clustering process. We highlight Ailon et al [2], which uses a query mechanism to reduce computational complexity, proposing a polynomial solution to an NP-hard problem. In Xiao and Dunham [111], the authors emphasize the need for an interactive clustering process for transactional data based on the fact that in many cases such data "may not be provided all at once to the clustering algorithm."…”
Section: Improving the Clustering Qualitymentioning
confidence: 99%
“…Other forms of user-specified cluster constraints come in the form of socalled "must-links" and/or "cannot-links" (e.g., Ailon et al [2], Basu et al [15], desJardins et al [35], Vikram and Dasgupta [103], Wang and Davidson [107], Xiong et al [112]). These constraints define that two or more samples must, or must not, be assigned to the same cluster.…”
Section: Add Constraintsmentioning
confidence: 99%
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