Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Weighted Association Rule Mining using weighted support and significance framework
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Algorithms for Frequent Itemset Mining Using FP-Trees
IEEE Transactions on Knowledge and Data Engineering
MAFIA: A Maximal Frequent Itemset Algorithm
IEEE Transactions on Knowledge and Data Engineering
Sequential Pattern Mining in Multi-Databases via Multiple Alignment
Data Mining and Knowledge Discovery
Mining top-K frequent itemsets from data streams
Data Mining and Knowledge Discovery
Informatics system comprehension: A learner-centred cognitive approach to networked thinking
Education and Information Technologies
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
Discovery of maximum length frequent itemsets
Information Sciences: an International Journal
Mining conjunctive sequential patterns
Data Mining and Knowledge Discovery
Efficient single-pass frequent pattern mining using a prefix-tree
Information Sciences: an International Journal
SeqStream: Mining Closed Sequential Patterns over Stream Sliding Windows
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Sliding window-based frequent pattern mining over data streams
Information Sciences: an International Journal
UP-Growth: an efficient algorithm for high utility itemset mining
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Frequent regular itemset mining
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining top-k frequent items in a data stream with flexible sliding windows
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Re-examination of interestingness measures in pattern mining: a unified framework
Data Mining and Knowledge Discovery
New approach for the sequential pattern mining of high-dimensional sequence databases
Decision Support Systems
An improved frequent pattern growth method for mining association rules
Expert Systems with Applications: An International Journal
Frequent Instruction Sequential Pattern Mining in Hardware Sample Data
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Weighted approximate sequential pattern mining within tolerance factors
Intelligent Data Analysis
Mining frequent patterns from univariate uncertain data
Data & Knowledge Engineering
Expert Systems with Applications: An International Journal
CEMiner -- An Efficient Algorithm for Mining Closed Patterns from Time Interval-Based Data
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Efficient Mining of Closed Sequential Patterns on Stream Sliding Window
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Mining regular patterns in data streams
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
An efficient mining algorithm for maximal weighted frequent patterns in transactional databases
Knowledge-Based Systems
Interactive mining of high utility patterns over data streams
Expert Systems with Applications: An International Journal
MapReduce-Based Balanced Mining for Closed Frequent Itemset
ICWS '12 Proceedings of the 2012 IEEE 19th International Conference on Web Services
Sliding window based weighted maximal frequent pattern mining over data streams
Expert Systems with Applications: An International Journal
Mining maximal frequent patterns by considering weight conditions over data streams
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
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Outstanding frequent pattern mining guarantees both fast runtime and low memory usage with respect to various data with different types and sizes. However, it is hard to improve the two elements since runtime is inversely proportional to memory usage in general. Researchers have made efforts to overcome the problem and have proposed mining methods which can improve both through various approaches. Many of state-of-the-art mining algorithms use tree structures, and they create nodes independently and connect them as pointers when constructing their own trees. Accordingly, the methods have pointers for each node in the trees, which is an inefficient way since they should manage and maintain numerous pointers. In this paper, we propose a novel tree structure to solve the limitation. Our new structure, LP-tree (Linear Prefix - Tree) is composed of array forms and minimizes pointers between nodes. In addition, LP-tree uses minimum information required in mining process and linearly accesses corresponding nodes. We also suggest an algorithm applying LP-tree to the mining process. The algorithm is evaluated through various experiments, and the experimental results show that our approach outperforms previous algorithms in term of the runtime, memory, and scalability.