Results 31 to 40 of about 846,106 (226)
A Frequent Itemset Hiding Toolbox [PDF]
, 2019 Advances in data collection and data storage technologies have given way to the establishment of transactional databases among companies and organizations, as they allow enormous amounts of data to be stored efficiently. Useful knowledge can be mined from these data, which can be used in several ways depending on the nature of the data.
Vasileios Kagklis +2 more
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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
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Deriving Frequent Itemsets from Lossless Condensed Representation
, 2020 In data mining, major research topic is frequent itemset mining (FIM). Frequent Itemsets (FIs) usually generating a large amount of Itemsets from database it causing from high memory and long execution time usage.
A. Subashini, M. Karthikeyan
core +1 more source
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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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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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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MINING FREQUENT itemsets using advanced partition APPROACH [PDF]
, 2009 Frequent itemsets mining plays an important part in many data mining tasks. This technique has been used in numerous practical applications, including market basket analysis. This paper presents mining frequent itemsets in large database of medical sales
Khin Myat Myat Moe +3 more
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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
Efficient heuristics for the Steiner forest problem
International Transactions in Operational Research, EarlyView.Abstract Let G=(V,E)$G = (V, E)$ be a connected undirected graph, V$V$ a set of nodes, E$E$ a set of edges, |V|=n$|V| = n$, and |E|=m$|E| = m$. Given a non‐negative weight function w:E→R+$w: E \rightarrow \mathbb {R}^+$ associated with its edges, a set τ={Ti⊆V|i=1,…,p}$\tau = \lbrace T_i \subseteq V | i = 1, \ldots, p\rbrace$ of terminal sets Ti$T_i ...
Murilo Stockinger +4 more
wiley +1 more source