Constrained frequent pattern mining: a pattern-growth view
ACM SIGKDD Explorations Newsletter
TreeITL-Mine: Mining Frequent Itemsets Using Pattern Growth, Tid Intersection, and Prefix Tree
AI '02 Proceedings of the 15th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Local and Global Methods in Data Mining: Basic Techniques and Open Problems
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
Mining frequent item sets by opportunistic projection
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
CT-ITL: efficient frequent item set mining using a compressed prefix tree with pattern growth
ADC '03 Proceedings of the 14th Australasian database conference - Volume 17
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
CLOSET+: searching for the best strategies for mining frequent closed itemsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
On computing, storing and querying frequent patterns
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Go Green: Recycle and Reuse Frequent Patterns
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Memory-adative association rules mining
Information Systems - Databases: Creation, management and utilization
Efficient closed pattern mining in the presence of tough block constraints
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining and Reasoning on Workflows
IEEE Transactions on Knowledge and Data Engineering
Efficient incremental maintenance of frequent patterns with FP-tree
Journal of Computer Science and Technology
Efficient calendar based temporal association rule
ACM SIGMOD Record
WAM-Miner: in the search of web access motifs from historical web log data
Proceedings of the 14th ACM international conference on Information and knowledge management
Fast and Memory Efficient Mining of Frequent Closed Itemsets
IEEE Transactions on Knowledge and Data Engineering
A Transaction Mapping Algorithm for Frequent Itemsets Mining
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering
Keeping things simple: finding frequent item sets by recursive elimination
Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations
An efficient approach to mining indirect associations
Journal of Intelligent Information Systems
Mining frequent tree-like patterns in large datasets
Data & Knowledge Engineering
A new approach to mine frequent patterns using item-transformation methods
Information Systems
Iceberg-cube algorithms: An empirical evaluation on synthetic and real data
Intelligent Data Analysis
A time- and memory-efficient frequent itemset discovering algorithm for association rule mining
International Journal of Computer Applications in Technology
Mining fault-tolerant frequent patterns efficiently with powerful pruning
Proceedings of the 2008 ACM symposium on Applied computing
A data mining proxy approach for efficient frequent itemset mining
The VLDB Journal — The International Journal on Very Large Data Bases
ON DATA STRUCTURES FOR ASSOCIATION RULE DISCOVERY
Applied Artificial Intelligence
Index-BitTableFI: An improved algorithm for mining frequent itemsets
Knowledge-Based Systems
Mining Supplemental Frequent Patterns
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
An efficient mining of weighted frequent patterns with length decreasing support constraints
Knowledge-Based Systems
FIUT: A new method for mining frequent itemsets
Information Sciences: an International Journal
Deriving strong association mining rules using a dependency criterion, the lift measure
International Journal of Data Analysis Techniques and Strategies
Frequent pattern mining with uncertain data
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Temporal Patterns for Humanoid Robot Using Pattern Growth Method
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Reducing rule covers with deterministic error bounds
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Depth first generation of frequent patterns without candidate generation
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
Maximum item first pattern growth for mining frequent patterns
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
An efficient frequent pattern mining algorithm
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 2
Frequent subtrees minging based on projected node
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 7
Using a cosine-type measure to derive strong association mining rules
International Journal of Knowledge Engineering and Data Mining
BISC: A bitmap itemset support counting approach for efficient frequent itemset mining
ACM Transactions on Knowledge Discovery from Data (TKDD)
Efficient temporal pattern mining for humanoid robot
Proceedings of the 1st Amrita ACM-W Celebration on Women in Computing in India
A new logic correlation rule for HIV-1 protease mutation
Expert Systems with Applications: An International Journal
Mining of frequent itemsets with JoinFI-mine algorithm
AIKED'11 Proceedings of the 10th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
Boosting performance in attack intention recognition by integrating multiple techniques
Frontiers of Computer Science in China
Equivalence class transformation based mining of frequent itemsets from uncertain data
Proceedings of the 2011 ACM Symposium on Applied Computing
Mining frequent patterns from univariate uncertain data
Data & Knowledge Engineering
Mining frequent trees based on topology projection
APWeb'05 Proceedings of the 7th Asia-Pacific web conference on Web Technologies Research and Development
A fast algorithm for mining share-frequent itemsets
APWeb'05 Proceedings of the 7th Asia-Pacific web conference on Web Technologies Research and Development
Ramp: high performance frequent itemset mining with efficient bit-vector projection technique
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Parallel mining of top-k frequent itemsets in very large text database
WAIM'05 Proceedings of the 6th international conference on Advances in Web-Age Information Management
A sampling-based method for mining frequent patterns from databases
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
Mining constrained graphs: the case of workflow systems
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
Partition-Based approach to processing batches of frequent itemset queries
FQAS'06 Proceedings of the 7th international conference on Flexible Query Answering Systems
Efficient pattern mining of uncertain data with sampling
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Shaping SQL-Based frequent pattern mining algorithms
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
Fast tree-based mining of frequent itemsets from uncertain data
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
Mining frequent itemsets over uncertain databases
Proceedings of the VLDB Endowment
Mining probabilistic datasets vertically
Proceedings of the 16th International Database Engineering & Applications Sysmposium
Proceedings of the WICSA/ECSA 2012 Companion Volume
ML-DS: a novel deterministic sampling algorithm for association rules mining
ICDM'12 Proceedings of the 12th Industrial conference on Advances in Data Mining: applications and theoretical aspects
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
DFP-Growth: an efficient algorithm for mining frequent patterns in dynamic database
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
Scalable technique to discover items support from trie data structure
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
AC-CS: an immune-inspired associative classification algorithm
ICARIS'12 Proceedings of the 11th international conference on Artificial Immune Systems
Mining frequent itemsets in large databases: The hierarchical partitioning approach
Expert Systems with Applications: An International Journal
Discovering frequent itemsets on uncertain data: a systematic review
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
Novel parallel method for mining frequent patterns on multi-core shared memory systems
DISCS-2013 Proceedings of the 2013 International Workshop on Data-Intensive Scalable Computing Systems
Mining frequent itemsets in data streams within a time horizon
Data & Knowledge Engineering
A time-efficient breadth-first level-wise lattice-traversal algorithm to discover rare itemsets
Data Mining and Knowledge Discovery
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Methods for efficient mining of frequent patterns have been studied extensively by many researchers. However, the previously proposed methods still encounter someperformance bottlenecks when mining databases with different data characteristics, such as dense vs. sparse, long vs. short patterns, memory-based vs. disk-based, etc.In this study, we propose a simple and novel hyper-linkeddata structure, H-struct , and a new mining algorithm, H-mine ,which takes advantage of this data structure anddynamically adjusts links in the mining process. A distinct feature of this method is that it has very limitedand precisely predictable space overhead and runs really fast in memory-based setting. Moreover, it ca be scaled up to very large databases by database partitioning, and whenthe data set becomes dense,(conditional)FP-trees can be constructed dynamically as part of the mining process. Our study shows that H-mine has high performance in various kinds of data, outperforms the previously developedalgorithms in different settings, and is highly scalable in mining large databases. This study also proposes a new datamining methodology, space-preserving mining ,which mayhave strong impact in the future development of efficient and scalable data mining methods.