Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
Integrating association rule mining with relational database systems: alternatives and implications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Rules in incomplete information systems
Information Sciences: an International Journal
CACTUS—clustering categorical data using summaries
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Turbo-charging vertical mining of large databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
Set-Oriented Mining for Association Rules in Relational Databases
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Knowledge Discovery in Databases: An Attribute-Oriented Approach
VLDB '92 Proceedings of the 18th 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
Data Organization and Access for Efficient Data Mining
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Mining Association Rules with Weighted Items
IDEAS '98 Proceedings of the 1998 International Symposium on Database Engineering & Applications
Fast vertical mining using diffsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
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Many data mining methods are dependent on recognizing frequent patterns. Frequent patterns lead to the discovery of association rules, strong rules, sequential episodes, and multi-dimensional patterns. All can play a critical role in helping corporate and scientific institutions to understand and analyze their data. Patterns should be discovered in time and space efficient manner. Discovered patterns have authentic value when they accurately describe data trends; and, do not exclusively reflect noise or chance encounters. Vertical data mining algorithms key advantage is that they can outperform their horizontal counterparts in terms of both time and space efficiency. Little work has addressed how incomplete data influences vertical data mining. Consequently, the quality and utility of vertical mining algorithms results remains ambiguous as real data sets often contain incomplete data. This paper considers how to establish methodologies that deal with incomplete data in vertical mining; additionally, it seeks to develop strategies for determining the maximal utilization that can be mined from a dataset based on how much and what data is missing.