Crowdminer: Mining association rules from the crowd

Yael Amsterdamer*, Yael Grossman, Tova Milo, Pierre Senellart

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review


This demo presents CrowdMiner, a system enabling the mining of interesting data patterns from the crowd. While traditional data mining techniques have been used extensively for finding patterns in classic databases, they are not always suitable for the crowd, mainly because humans tend to remember only simple trends and summaries rather than exact details. To address this, CrowdMiner employs a novel crowd-mining algorithm, designed specifically for this context. The algorithm iteratively chooses appropriate questions to ask the crowd, while aiming to maximize the knowledge gain at each step. We demonstrate CrowdMiner through a Well-Being portal, constructed interactively by mining the crowd, and in particular the conference participants, for common health related practices and trends.

Original languageEnglish
Pages (from-to)1250-1253
Number of pages4
JournalProceedings of the VLDB Endowment
Issue number12
StatePublished - Aug 2013
Event39th International Conference on Very Large Data Bases, VLDB 2012 - Trento, Italy
Duration: 26 Aug 201330 Aug 2013


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