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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
11571
Venue
VLDB
Year
2017
Pagerank
4.3399748e-05
Overall Rank
9,426 | 34.49%
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
11,469 Fast Density-Peaks Clustering: Multicore-based Parallelization Approach 2021 SIGMOD 4.1905499e-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
159 LOF: Identifying Density-Based Local Outliers 2000 SIGMOD 0.00040135453
340 CURE: An Efficient Clustering Algorithm for Large Databases 1998 SIGMOD 0.00026854084
697 Efficient Algorithms for Mining Outliers from Large Data Sets 2000 SIGMOD 0.00017964755
768 Algorithms for Mining Distance-Based Outliers in Large Datasets 1998 VLDB 0.00016864875
1,375 SQLEM: Fast Clustering in SQL using the EM Algorithm 2000 SIGMOD 0.00012321024
2,146 Scalable K-Means++ 2012 VLDB 9.4341455e-05
2,826 Finding Intensional Knowledge of Distance-Based Outliers 1999 VLDB 8.0562807e-05
4,549 Outlier Detection for High Dimensional Data 2001 SIGMOD 6.0866685e-05
5,768 Outlier-robust Clustering using Independent Components 2008 SIGMOD 5.3332593e-05
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