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Local Search Methods for k-Means with Outliers

Summary: Proposes a simple local-search algorithm for k-means with outliers, delivering constant-factor guarantees for the inlier clustering. When paired with sketching techniques, it scales to large data and empirically dominates recent heuristics on synthetic and real-world benchmarks. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11570
Venue
VLDB
Year
2017
Pagerank
4.3441378e-05
Overall Rank
9,420 | 34.47%
DOI
-

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
11,466 Fast Density-Peaks Clustering: Multicore-based Parallelization Approach 2021 SIGMOD 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 9 of 9 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
161 LOF: Identifying Density-Based Local Outliers 2000 SIGMOD 0.00039846974
341 CURE: An Efficient Clustering Algorithm for Large Databases 1998 SIGMOD 0.00026810548
701 Efficient Algorithms for Mining Outliers from Large Data Sets 2000 SIGMOD 0.00017938417
774 Algorithms for Mining Distance-Based Outliers in Large Datasets 1998 VLDB 0.00016865771
1,372 SQLEM: Fast Clustering in SQL using the EM Algorithm 2000 SIGMOD 0.00012318334
2,093 Scalable K-Means++ 2012 VLDB 9.5588104e-05
2,822 Finding Intensional Knowledge of Distance-Based Outliers 1999 VLDB 8.0608136e-05
4,552 Outlier Detection for High Dimensional Data 2001 SIGMOD 6.0922282e-05
5,760 Outlier-robust Clustering using Independent Components 2008 SIGMOD 5.3382727e-05
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