Results 41 to 50 of about 45,837 (241)
DiffNodesets: An Efficient Structure for Fast Mining Frequent Itemsets
, 2015 Mining frequent itemsets is an essential problem in data mining and plays an important role in many data mining applications. In recent years, some itemset representations based on node sets have been proposed, which have shown to be very efficient for ...
Deng, Zhi-Hong
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Mining Target-Oriented Sequential Patterns with Time-Intervals [PDF]
, 2010 A target-oriented sequential pattern is a sequential pattern with a concerned itemset in the end of pattern. A time-interval sequential pattern is a sequential pattern with time-intervals between every pair of successive itemsets.
Chueh, Hao-En
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A weighted frequent itemset mining algorithm for intelligent decision in smart systems
IEEE Access, 2018 Intelligent decision is the key technology of smart systems. Data mining technology has been playing an increasingly important role in decision-making activities.
Xuejian Zhao +4 more
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Mining Top-K Frequent Itemsets Through Progressive Sampling
, 2010 We study the use of sampling for efficiently mining the top-K frequent itemsets of cardinality at most w. To this purpose, we define an approximation to the top-K frequent itemsets to be a family of itemsets which includes (resp., excludes) all very ...
Andrea Pietracaprina +8 more
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An Efficient Rigorous Approach for Identifying Statistically Significant Frequent Itemsets [PDF]
, 2009 As advances in technology allow for the collection, storage, and analysis of vast amounts of data, the task of screening and assessing the significance of discovered patterns is becoming a major challenge in data mining applications.
Kirsch, Adam +5 more
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Mining Frequent Itemsets Using Genetic Algorithm
, 2010 In general frequent itemsets are generated from large data sets by applying association rule mining algorithms like Apriori, Partition, Pincer-Search, Incremental, Border algorithm etc., which take too much computer time to compute all the frequent ...
Biswas, Sushanta +3 more
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Incremental Closed Frequent Itemsets Mining-Based Approach Using Maximal Candidates
IEEE AccessIncremental frequent itemset mining aims to efficiently update frequent itemsets without recalculating them from scratch, making it suitable for streaming data and real-time analytics.
Mohammed A. Al-Zeiadi +1 more
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Proposed Algorithm for Extracting Association Rule Depend on Closed Frequent Itemset (EACFI) [PDF]
Engineering and Technology Journal, 2011 Association rules are important one of data mining activities. All algorithms of association rule mining consist of finding frequency of itemsets, which satisfy a minimum support threshold, and then compute confidence percentage for each k-itemsets to ...
Emad k. Jbbar, Yaser Munther
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An Efficient Spark-Based Hybrid Frequent Itemset Mining Algorithm for Big Data
Data, 2022 Frequent itemset mining (FIM) is a common approach for discovering hidden frequent patterns from transactional databases used in prediction, association rules, classification, etc. Apriori is an FIM elementary algorithm with iterative nature used to find
Mohamed Reda Al-Bana +2 more
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Quantum algorithm for association rules mining
, 2016 Association rules mining (ARM) is one of the most important problems in knowledge discovery and data mining. Given a transaction database that has a large number of transactions and items, the task of ARM is to acquire consumption habits of customers by ...
Gao, Fei +3 more
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