Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Using association rules for product assortment decisions: a case study
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Evaluation of sampling for data mining of association rules
RIDE '97 Proceedings of the 7th International Workshop on Research Issues in Data Engineering (RIDE '97) High Performance Database Management for Large-Scale Applications
Selective Sampling for Nearest Neighbor Classifiers
Machine Learning
Numerical computation of rectangular bivariate and trivariate normal and t probabilities
Statistics and Computing
Orange: from experimental machine learning to interactive data mining
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
The Wisdom of Crowds
Yago: a core of semantic knowledge
Proceedings of the 16th international conference on World Wide Web
Designing games with a purpose
Communications of the ACM - Designing games with a purpose
Get another label? improving data quality and data mining using multiple, noisy labelers
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Crowdsourcing systems on the World-Wide Web
Communications of the ACM
CrowdDB: answering queries with crowdsourcing
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Multi-armed bandit algorithms and empirical evaluation
ECML'05 Proceedings of the 16th European conference on Machine Learning
Declarative platform for data sourcing games
Proceedings of the 21st international conference on World Wide Web
Asking the Right Questions in Crowd Data Sourcing
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
CrowdMiner: mining association rules from the crowd
Proceedings of the VLDB Endowment
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Harnessing a crowd of Web users for data collection has recently become a wide-spread phenomenon. A key challenge is that the human knowledge forms an open world and it is thus difficult to know what kind of information we should be looking for. Classic databases have addressed this problem by data mining techniques that identify interesting data patterns. These techniques, however, are not suitable for the crowd. This is mainly due to properties of the human memory, such as the tendency to remember simple trends and summaries rather than exact details. Following these observations, we develop here for the first time the foundations of crowd mining. We first define the formal settings. Based on these, we design a framework of generic components, used for choosing the best questions to ask the crowd and mining significant patterns from the answers. We suggest general implementations for these components, and test the resulting algorithm's performance on benchmarks that we designed for this purpose. Our algorithm consistently outperforms alternative baseline algorithms.