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
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Knowledge Discovery and Measures of Interest
Knowledge Discovery and Measures of Interest
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Finding Interesting Patterns Using User Expectations
IEEE Transactions on Knowledge and Data Engineering
Alternative Interest Measures for Mining Associations in Databases
IEEE Transactions on Knowledge and Data Engineering
Mining association rules on significant rare data using relative support
Journal of Systems and Software
Efficient Algorithms for Mining Closed Itemsets and Their Lattice Structure
IEEE Transactions on Knowledge and Data Engineering
TFP: An Efficient Algorithm for Mining Top-K Frequent Closed Itemsets
IEEE Transactions on Knowledge and Data Engineering
A survey of interestingness measures for knowledge discovery
The Knowledge Engineering Review
On Mining Summaries by Objective Measures of Interestingness
Machine Learning
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
High-utility pattern mining: A method for discovery of high-utility item sets
Pattern Recognition
Discovery of maximum length frequent itemsets
Information Sciences: an International Journal
Advertising keyword suggestion based on concept hierarchy
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Discovering Periodic-Frequent Patterns in Transactional Databases
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Mining periodic-frequent patterns with maximum items' support constraints
Proceedings of the Third Annual ACM Bangalore Conference
Mining the k-most interesting frequent patterns sequentially
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
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In the area of data mining, the process of frequent pattern extraction finds interesting information about the association among the items in a transactional database. The notion of support is employed to extract the frequent patterns. Normally, a frequent pattern may contain items which belong to different categories of a particular domain. The existing approaches do not consider the notion of diversity while extracting the frequent patterns. For certain types of applications, it may be useful to distinguish between the frequent patterns with items belonging to different categories and the frequent patterns with items belonging to the same category. In this paper we propose a new interestingness measure, called DiverseRank, to rank the frequent patterns based on the items' categories. Given a set of frequent patterns, we propose an efficient algorithm to extract the diverse-frequent patterns. Experiments on the real-world data set show that the diverse-frequent patterns extracted with the proposed DiverseRank measure are different from the frequent patterns extracted with the support measure.