ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Approximating a collection of frequent sets
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
SPIN: mining maximal frequent subgraphs from graph databases
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Summarizing itemset patterns: a profile-based approach
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Summarization — Compressing Data into an Informative Representation
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Frequency-based views to pattern collections
Discrete Applied Mathematics - Special issue: Discrete mathematics & data mining II (DM & DM II)
Constraint-based sequential pattern mining: the consideration of recency and compactness
Decision Support Systems
Efficient mining of understandable patterns from multivariate interval time series
Data Mining and Knowledge Discovery
A new concise representation of frequent itemsets using generators and a positive border
Knowledge and Information Systems
Blind paraunitary equalization
Signal Processing
Non-Derivable Item Set and Non-Derivable Literal Set Representations of Patterns Admitting Negation
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
ACM Transactions on Knowledge Discovery from Data (TKDD)
Frequency-based views to pattern collections
Discrete Applied Mathematics - Special issue: Discrete mathematics & data mining II (DM & DM II)
Using a reinforced concept lattice to incrementally mine association rules from closed itemsets
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
CLAIM: an efficient method for relaxed frequent closed itemsets mining over stream data
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Graph summaries for subgraph frequency estimation
ESWC'08 Proceedings of the 5th European semantic web conference on The semantic web: research and applications
MARGIN: Maximal frequent subgraph mining
ACM Transactions on Knowledge Discovery from Data (TKDD)
Approximate weighted frequent pattern mining with/without noisy environments
Knowledge-Based Systems
An automata approach to pattern collections
KDID'04 Proceedings of the Third international conference on Knowledge Discovery in Inductive Databases
Implicit enumeration of patterns
KDID'04 Proceedings of the Third international conference on Knowledge Discovery in Inductive Databases
COBRA: closed sequential pattern mining using bi-phase reduction approach
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Transaction databases, frequent itemsets, and their condensed representations
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
Anytime algorithms for mining groups with maximum coverage
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
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Frequent pattern mining has been studied extensively.However, the effectiveness and efficiency of this mining isoften limited, since the number of frequent patterns generatedis often too large. In many applications it is sufficientto generate and examine only frequent patterns with supportfrequency in close-enough approximation instead of in fullprecision. Such a compact but close-enough frequent patternbase is called a condensed frequent patterns-base.In this paper, we propose and examine several alternativesat the design, representation, and implementation ofsuch condensed frequent pattern-bases. A few algorithmsfor computing such pattern-bases are proposed. Their effectivenessat pattern compression and their efficient computationmethods are investigated. A systematic performancestudy is conducted on different kinds of databases,which demonstrates the effectiveness and efficiency of ourapproach at handling frequent pattern mining in largedatabases.