CrowdMiner: Mining association rules from the crowd

Summary: CrowdMiner introduces a novel crowd-mining algorithm for association rules from crowd data, not static databases. An iterative, question-driven querying process maximizes knowledge gain, demonstrated via a Well-Being portal mining health trends among conference participants. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 10773
Venue: VLDB
Year: 2013
Pagerank: 5.3726124e-05
Overall Rank: 8,789 | 39.70%
DOI: 10.14778/2536274.2536288

Incoming Non-self Citations Over Time

Authors

1. Yael Amsterdamer (Tel Aviv University)
2. Yael Grossman (Tel Aviv University)
3. Tova Milo (Tel Aviv University)
4. Pierre Senellart (Telecom ParisTech; University of Hong Kong)

BibTeX Citation

@article{amsterdamer_vldb13,
        title = {{CrowdMiner: Mining association rules from the crowd}},
        author = {Amsterdamer, Yael and Grossman, Yael and Milo, Tova and Senellart, Pierre},
        journal = {PVLDB},
        series = {{VLDB} '13},
        volume = {6},
        number = {12},
        pages = {1250--1253},
        doi = {10.14778/2536274.2536288},
        url = {https://doi.org/10.14778/2536274.2536288},
        year = {2013}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
7,357	Crowdsourced Data Management: Overview and Challenges	2017	SIGMOD	5.6346837e-05
7,729	Subjective Knowledge Base Construction Powered By Crowdsourcing and Knowledge Base	2018	SIGMOD	5.5574654e-05

Outgoing Citations (Sorted by Pagerank)

Showing 5 of 5 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
27	Fast Algorithms for Mining Association Rules	1994	VLDB	0.00052255472
90	CrowdDB: Answering Queries with Crowdsourcing	2011	SIGMOD	0.00034951786
250	Answering Queries using Humans, Algorithms and Databases	2011	CIDR	0.00023261164
462	Sampling Large Databases for Association Rules	1996	VLDB	0.00018065337
2,789	Crowd Mining	2013	SIGMOD	8.1203892e-05

Semantically Similar Papers

#	Overall Rank	Paper	Year	Venue
1	11,925	Crowdsourcing Analytics with CrowdCur	2018	SIGMOD
2	5,474	CrowdQ: Crowdsourced Query Understanding	2013	CIDR
3	4,568	CrowdMatcher: Crowd-Assisted Schema Matching	2014	SIGMOD
4	13,739	MobileMiner: A Real World Case Study of Data Mining in Mobile Communication	2009	SIGMOD
5	6,719	Exploratory Mining via Constrained Frequent Set Queries	1999	SIGMOD
6	10,010	Large Scale Graph Mining with G-Miner	2019	SIGMOD
7	8,788	Managing General and Individual Knowledge in Crowd Mining Applications	2015	CIDR
8	9,275	Ontology Assisted Crowd Mining	2014	VLDB
9	7,081	OASSIS: Query Driven Crowd Mining	2014	SIGMOD
10	2,789	Crowd Mining	2013	SIGMOD