A New Sparse Data Clustering Method Based On Frequent Items

Summary: Proposes k-FreqItems, a scalable clustering method for high-dimensional, sparse categorical data using a sparse FreqItem center and Jaccard distance for interpretable clusters. SILK, an LSH-based seeding technique, oversamples frequent co-occurrences to seed k-FreqItems, delivering faster, more effective initialization and billion-object scalability on commodity GPUs (code: https://github.com/HuangQiang/k-FreqItems). (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: h8e9e5688a29cc64d
Venue: SIGMOD
Year: 2023
Pagerank: 5.580601e-05
Overall Rank: 7,121 | 52.27%
DOI: 10.1145/3588685
PDF: Download (CC BY-NC-SA 4.0)

Incoming Non-self Citations Over Time

Authors

1. Qiang Huang (National University of Singapore)
2. Pingyi Luo (National University of Singapore)
3. Anthony K. H. Tung (National University of Singapore)

BibTeX Citation

@inproceedings{huang_sigmod23,
        title = {{A New Sparse Data Clustering Method Based On Frequent Items}},
        author = {Huang, Qiang and Luo, Pingyi and Tung, Anthony K. H.},
        series = {{SIGMOD} '23},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3588685},
        url = {https://dl.acm.org/doi/10.1145/3588685},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
11,275	SBSC: A fast Self-tuned Bipartite proximity graph-based Spectral Clustering	2025	SIGMOD	4.9605343e-05

Outgoing Citations (Sorted by Pagerank)

Showing 19 of 19 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
29	Fast Algorithms for Mining Association Rules	1994	VLDB	0.00051028332
32	BIRCH: An Efficient Data Clustering Method for Very Large Databases	1996	SIGMOD	0.00049559562
281	Multi-Probe LSH: Efficient Indexing for High-Dimensional Similarity Search	2007	VLDB	0.00022262975
286	Query-Aware Locality-Sensitive Hashing for Approximate Nearest Neighbor Search	2016	VLDB	0.00022090962
300	OPTICS: Ordering Points To Identify the Clustering Structure	1999	SIGMOD	0.00021743411
339	Locality-Sensitive Hashing Scheme Based on Dynamic Collision Counting	2012	SIGMOD	0.00020558018
364	CURE: An Efficient Clustering Algorithm for Large Databases	1998	SIGMOD	0.00019916561
562	SRS: Solving c-Approximate Nearest Neighbor Queries in High Dimensional Euclidean Space with a Tiny Index	2015	VLDB	0.00016344855
578	Quality and Efficiency in High Dimensional Nearest Neighbor Search	2009	SIGMOD	0.00016079505
992	DBSCAN Revisited: Mis-Claim, Un-Fixability, and Approximation	2015	SIGMOD	0.00012621584
1,318	VHP: Approximate Nearest Neighbor Search via Virtual Hypersphere Partitioning	2020	VLDB	0.00011035606
1,460	PM-LSH: A Fast and Accurate LSH Framework for High-Dimensional Approximate NN Search	2020	VLDB	0.00010558941
1,524	LazyLSH: Approximate Nearest Neighbor Search for Multiple Distance Functions with a Single Index	2016	SIGMOD	0.0001033427
2,138	Scalable K-Means++	2012	VLDB	8.9755133e-05
2,623	NG-DBSCAN: Scalable Density-Based Clustering for Arbitrary Data	2017	VLDB	8.196568e-05
2,968	Locality-Sensitive Hashing Scheme based on Longest Circular Co-Substring	2020	SIGMOD	7.7879589e-05
5,227	Point-to-Hyperplane Nearest Neighbor Search Beyond the Unit Hypersphere	2021	SIGMOD	6.2080477e-05
5,294	FARGO: Fast Maximum Inner Product Search via Global Multi-Probing	2023	VLDB	6.1780995e-05
9,710	MQH: Locality Sensitive Hashing on Multi-level Quantization Errors for Point-to-Hyperplane Distances	2023	VLDB	5.1246696e-05

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3	11,275	SBSC: A fast Self-tuned Bipartite proximity graph-based Spectral Clustering	2025	SIGMOD
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7	10,610	SieveSketch: A Fine-grained and Adaptive Sketch Framework for Accurate Frequency Estimation	2026	SIGMOD
8	1,685	Fast Algorithms for Projected Clustering	1999	SIGMOD
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