Parametric Algorithms for Mining Share Frequent Itemsets
Journal of Intelligent Information Systems
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
A fast high utility itemsets mining algorithm
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
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ACM Computing Surveys (CSUR)
Mining itemset utilities from transaction databases
Data & Knowledge Engineering - Special issue: ER 2003
Isolated items discarding strategy for discovering high utility itemsets
Data & Knowledge Engineering
Mining long high utility itemsets in transaction databases
SMO'07 Proceedings of the 7th WSEAS International Conference on Simulation, Modelling and Optimization
Pushing Frequency Constraint to Utility Mining Model
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
Mining long high utility itemsets in transaction databases
WSEAS Transactions on Information Science and Applications
Mining high utility patterns in incremental databases
Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication
Discovering association patterns based on mutual information
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
Two-phase algorithms for a novel utility-frequent mining model
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
A test paradigm for detecting changes in transactional data streams
DASFAA'08 Proceedings of the 13th international conference on Database systems for advanced applications
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DaWaK'10 Proceedings of the 12th international conference on Data warehousing and knowledge discovery
EWgen: automatic generation of item weights for weighted association rule mining
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Effective utility mining with the measure of average utility
Expert Systems with Applications: An International Journal
HUC-Prune: an efficient candidate pruning technique to mine high utility patterns
Applied Intelligence
Efficient prime-based method for interactive mining of frequent patterns
Expert Systems with Applications: An International Journal
A fast algorithm for mining share-frequent itemsets
APWeb'05 Proceedings of the 7th Asia-Pacific web conference on Web Technologies Research and Development
Direct candidates generation: a novel algorithm for discovering complete share-frequent itemsets
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
A two-phase algorithm for fast discovery of high utility itemsets
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Mining high utility itemsets without candidate generation
Proceedings of the 21st ACM international conference on Information and knowledge management
ShrFP-tree: an efficient tree structure for mining share-frequent patterns
AusDM '08 Proceedings of the 7th Australasian Data Mining Conference - Volume 87
Expert Systems with Applications: An International Journal
Mining high utility itemsets by dynamically pruning the tree structure
Applied Intelligence
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Itemset share has been proposed as an additional measure of the importance of itemsets in association rule mining (Carter et al., 1997). We compare the share and support measures to illustrate that the share measure can provide useful information about numerical values that are typically associated with transaction items, which the support measure cannot. We define the problem of finding share frequent itemsets, and show that share frequency does not have the property of downward closure when it is defined in terms of the itemset as a whole. We present algorithms that do not rely on the property of downward closure, and thus are able to find share frequent itemsets that have infrequent subsets. The algorithms use heuristic methods to generate candidate itemsets. They supplement the information contained in the set of frequent itemsets from a previous pass, with other information that is available at no additional processing cost. They count only those generated itemsets that are predicted to be frequent. The algorithms are applied to a large commercial database and their effectiveness is examined using principles of classifier evaluation from machine learning.