Results 81 to 90 of about 8,833 (199)
ASCF: Optimization of the Apriori Algorithm Using Spark‐Based Cuckoo Filter Structure
International Journal of Intelligent Systems, Volume 2024, Issue 1, 2024.Data mining is the process used for extracting hidden patterns from large databases using a variety of techniques. For example, in supermarkets, we can discover the items that are often purchased together and that are hidden within the data. This helps make better decisions which improve the business outcomes.
Bana Ahmad Alrahwan +2 more
wiley +1 more source
Interactive Constrained Association Rule Mining
, 2003 We investigate ways to support interactive mining sessions, in the setting of association rule mining. In such sessions, users specify conditions (queries) on the associations to be generated.
Bussche, Jan Van den, Goethals, Bart
core +3 more sources
Association rules recommendation algorithm supporting recommendation nonempty
Tongxin xuebao, 2017 Existing association rule recommendation technologies were focus on extraction efficiency of association rule in data mining.However,it lacked consideration of recommendation balance between popular and unusual data and efficient processing.In order to ...
Ming HE, Wei-shi LIU, Jiang ZHANG
doaj +2 more sources
Research on Frequent Itemset Mining of Imaging Genetics GWAS in Alzheimer's Disease. [PDF]
Genes (Basel), 2022 Liang H +7 more
europepmc +1 more source
TKFIM: Top-K frequent itemset mining technique based on equivalence classes. [PDF]
PeerJ Comput Sci, 2021 Iqbal S +5 more
europepmc +1 more source
FREQUENT ITEMSETS MINING FOR BIG DATA
, 2019 Frequent Itemsets Mining (FIM) is a fundamental mining model and plays an important role in Data Mining. It has a vast range of application fields and can be employed as a key calculation phase in many other mining models such as Association Rules, Correlations, Classifications, etc. Generally speaking, FIM counts the frequencies of co-occurrence items,
openaire +2 more sources
An Evolutionary Algorithm to Mine High-Utility Itemsets
Advances in Electrical and Electronic Engineering, 2015 High-utility itemset mining (HUIM) is a critical issue in recent years since it can be used to reveal the profitable products by considering both the quantity and profit factors instead of frequent itemset mining (FIM) of association rules (ARs). In this
Jerry Chun-Wei Lin +5 more
doaj +1 more source
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
doaj +1 more source
TRICE: Mining Frequent Itemsets by Iterative TRimmed Transaction LattICE in Sparse Big Data
IEEE Access, 2019 Sparseness is often witnessed in big data emanating from a variety of sources, including IoT, pervasive computing, and behavioral data. Frequent itemset mining is the first and foremost step of association rule mining, which is a distinguished ...
Muhammad Yasir +7 more
doaj +1 more source
Characterizing Transactional Databases for Frequent Itemset Mining
, 2019 This paper presents a study of the characteristics of transactional databases used in frequent itemset mining. Such characterizations have typically been used to benchmark and understand the data mining algorithms working on these databases. The aim of our study is to give a picture of how diverse and representative these benchmarking databases are ...
Lezcano Ríos, Christian Gerardo +1 more
openaire +3 more sources