Results 31 to 40 of about 2,597 (181)
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
core +1 more source
Binary image description using frequent itemsets
Journal of Big Data, 2020 In this paper, a novel method for binary image comparison is presented. We suppose that the image is a set of transactions and items. The proposed method applies along rows and columns of an image; this image is represented by all frequent itemset ...
Khalid Aznag +3 more
doaj +1 more source
Sliding Window-based Frequent Itemsets Mining over Data Streams using Tail Pointer Table [PDF]
International Journal of Computational Intelligence Systems, 2014 Mining frequent itemsets over transaction data streams is critical for many applications, such as wireless sensor networks, analysis of retail market data, and stock market predication.
Le Wang, Lin Feng, Bo Jin
doaj +1 more source
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
openaire +3 more sources
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
doaj +1 more source
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
doaj +1 more source
Reading Research Quarterly, Volume 61, Issue 2, April/May/June 2026.This study introduces and validates the Self‐Efficacy for Online Reading Questionnaire (SEORQ), a process‐grounded instrument designed to measure secondary students' efficacy in executing the core demands of online reading. The model conceptualizes online reading self‐efficacy as a multidimensional construct encompassing five interrelated processes ...
SeongYeup Kim +2 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
doaj +1 more source
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
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.
Benjamin Schlegel +2 more
openaire +3 more sources