Results 31 to 40 of about 794 (166)
Mining Productive Itemsets in Dynamic Databases
IEEE Access, 2020 Discovering frequent itemsets is a data analysis task used in numerous domains. It consists of finding sets of items (itemsets) that frequently appear in a set of database records (also called transactions). Though discovering frequent itemsets is useful,
Xiang Li +5 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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Frequent regular itemset mining [PDF]
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, 2010 Concise representations of frequent itemsets sacrifice readability and direct interpretability by a data analyst of the concise patterns extracted. In this paper, we introduce an extension of itemsets, called regular, with an immediate semantics and interpretability, and a conciseness comparable to closed itemsets. Regular itemsets allow for specifying
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Mining frequent itemsets over uncertain databases [PDF]
Proceedings of the VLDB Endowment, 2012 In recent years, due to the wide applications of uncertain data, mining frequent itemsets over uncertain databases has attracted much attention. In uncertain databases, the support of an itemset is a random variable instead of a fixed occurrence counting of this itemset.
Tong, Yongxin +3 more
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Critical Review for One‐Class Classification: Recent Advances and Reality Behind Them
WIREs Data Mining and Knowledge Discovery, Volume 16, Issue 1, March 2026.This review presents a new taxonomy to summarize one‐class classification (OCC) algorithms and their applications. The main argument is that OCC should not learn multiple classes. The paper highlights common violations of OCC involving multiple classes.
Toshitaka Hayashi +3 more
wiley +1 more source
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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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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A GENERAL SURVEY ON FREQUENT PATTERN MINING USING GENETIC ALGORITHM [PDF]
ICTACT Journal on Soft Computing, 2012 In recent years, data mining is an important aspect for generating association rules among the large number of itemsets. Association rule mining is one of the techniques in data mining that that has two sub processes. First, the process called as finding
K. Poornamala, R. Lawrance
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An Association Rule Mining Algorithm Based on a Boolean Matrix
Data Science Journal, 2007 Association rule mining is a very important research topic in the field of data mining. Discovering frequent itemsets is the key process in association rule mining.
Hanbing Liu, Baisheng Wang
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Geoscience Data Journal, Volume 13, Issue 1, January 2026.We build a new, open‐source global copper deposit dataset (GCDD), facilitating AI‐driven data analysis for exploration targeting and improving our understanding of copper mineralizing systems and their mappable expressions. The GCDD hosts information about 1483 copper deposits worldwide, capturing key deposit attributes such as location, genetic type ...
Bin Wang +2 more
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