Results 11 to 20 of about 794 (166)
Behavior Decoding Delineates Seizure Microfeatures and Associated Sudden Death Risks in Mouse Models of Epilepsy. [PDF]
Ann NeurolObjective Behavior and motor manifestations are distinctive yet often overlooked features of epileptic seizures. Seizures can result in transient disruptions in motor control, often organized into specific behavioral sequences that can inform seizure types, onset zones, and outcomes.
Shen Y +8 more
europepmc +2 more sources
Multi-Objective Optimization for High-Dimensional Maximal Frequent Itemset Mining
Applied Sciences, 2021 The solution space of a frequent itemset generally presents exponential explosive growth because of the high-dimensional attributes of big data. However, the premise of the big data association rule analysis is to mine the frequent itemset in high ...
Yalong Zhang +4 more
doaj +1 more source
Concept Lattice Method for Spatial Association Discovery in the Urban Service Industry
ISPRS International Journal of Geo-Information, 2020 A relative lag in research methods, technical means and research paradigms has restricted the rapid development of geography and urban computing. Hence, there is a certain gap between urban data and industry applications.
Weihua Liao, Zhiheng Zhang, Weiguo Jiang
doaj +1 more source
arules - A Computational Environment for Mining Association Rules and Frequent Item Sets
Journal of Statistical Software, 2005 Mining frequent itemsets and association rules is a popular and well researched approach for discovering interesting relationships between variables in large databases.
Michael Hahsler +2 more
doaj +1 more source
Efficiently Mining Frequent Itemsets on Massive Data
IEEE Access, 2019 Frequent itemset mining is an important operation to return all itemsets in the transaction table, which occur as a subset of at least a specified fraction of the transactions.
Xixian Han +5 more
doaj +1 more source
A Parallel Apriori Algorithm and FP- Growth Based on SPARK [PDF]
ITM Web of Conferences, 2021 Frequent Itemset Mining is an important data mining task in real-world applications. Distributed parallel Apriori and FP-Growth algorithm is the most important algorithm that works on data mining for finding the frequent itemsets.
Gupta Priyanka, Sawant Vinaya
doaj +1 more source
Memory-efficient frequent-itemset mining
Proceedings of the 14th International Conference on Extending Database Technology, 2011 Efficient discovery of frequent itemsets in large datasets is a key component of many data mining tasks. In-core algorithms---which operate entirely in main memory and avoid expensive disk accesses---and in particular the prefix tree-based algorithm FP-growth are generally among the most efficient of the available algorithms.
Schlegel, Benjamin +2 more
openaire +3 more sources
Efficiently mining association rules based on maximum single constraints
Vietnam Journal of Computer Science, 2017 A serious problem encountered during the mining of association rules is the exponential growth of their cardinality. Unfortunately, the known algorithms for mining association rules typically generate scores of redundant and duplicate rules. Thus, we not
Anh Tran, Tin Truong, Bac Le
doaj +1 more source
Efficient Mining of Frequent Itemsets Using Only One Dynamic Prefix Tree
IEEE Access, 2020 Frequent itemset mining is a fundamental problem in data mining area because frequent itemsets have been extensively used in reasoning, classifying, clustering, and so on.
Jun-Feng Qu +5 more
doaj +1 more source
MAXLEN-FI: AN ALGORITHM FOR MINING MAXIMUM- LENGTH FREQUENT ITEMSETS FAST
Tạp chí Khoa học Đại học Đà Lạt, 2018 Association rule mining, one of the most important and well-researched techniques of data mining. Mining frequent itemsets are one of the most fundamental and most time-consuming problems in association rule mining.
Phan Thành Huấn, Lê Hoài Bắc
doaj +1 more source