Results 51 to 60 of about 1,488,653 (205)
IEEE AccessTraditional pattern mining algorithms are based on tree and linked list structures. However, they often only consider a single factor of frequency or utility and have to deal with exponential search spaces as well as generate numerous candidates.
Xiumei Zhao, Xincheng Zhong, Bing Han
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Mining Frequent Itemsets for Evolving Database Involving Insertion [PDF]
, 2015 Mining frequent itemsets is one of the popular task in data mining. There are many applications like location-based services, sensor monitoring systems, and data integration in which the content of transaction is uncertain in nature.
, Mrs. Ashlesha A. Jagdale, Prof. Sonali Patil
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Inverted Index Automata Frequent Itemset Mining for Large Dataset Frequent Itemset Mining
IEEE AccessFrequent itemset mining (FIM) faces significant challenges with the expansion of large-scale datasets. Traditional algorithms such as Apriori, FP-Growth, and Eclat suffer from poor scalability and low efficiency when applied to modern datasets characterized by high dimensionality and high-density features.
Xin Dai 0007 +3 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 ...
PIETRACAPRINA, ANDREA ALBERTO +3 more
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International Journal of Computational Intelligence SystemsIn the process of data extraction, the rigid partitioning mechanism of fixed time windows leads to spatiotemporal heterogeneity mismatches in data distribution, resulting in semantic confusion and redundancy accumulation in mining results. To address the
Jie Zhang +3 more
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Negative and Positive Association Rules Mining from Text Using Frequent and Infrequent Itemsets
The Scientific World Journal, 2014 Association rule mining research typically focuses on positive association rules (PARs), generated from frequently occurring itemsets. However, in recent years, there has been a significant research focused on finding interesting infrequent itemsets ...
Sajid Mahmood +2 more
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FCHUIM: Efficient Frequent and Closed High-Utility Itemsets Mining
IEEE Access, 2020 Mining a closed high-utility itemset is a prevalent research task in analyzing transaction databases. However, numerous target itemsets are generated in the closed high-utility itemset mining task.
Tianyou Wei +5 more
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Mining frequent itemsets using the N-list and subsume concepts [PDF]
, 2016 Frequent itemset mining is a fundamental element with respect to many data mining problems directed at finding interesting patterns in data. Recently the PrePost algorithm, a new algorithm for mining frequent itemsets based on the idea of N-lists, which ...
Coenen, F, Vo, B, Le, T, Hong, TP
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Mining frequent itemsets from streaming transaction data using genetic algorithms
Journal of Big Data, 2020 This paper presents a study of mining frequent itemsets from streaming data in the presence of concept drift. Streaming data, being volatile in nature, is particularly challenging to mine.
Sikha Bagui, Patrick Stanley
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Efficiently Mining Maximal Diverse Frequent Itemsets [PDF]
, 2019 Given a database of transactions, where each transaction is a set of items, maximal frequent itemset mining aims to find all itemsets that are frequent, meaning that they consist of items that co-occur in transactions more often than a given threshold ...
Wu, Dingming +7 more
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