Results 81 to 90 of about 8,898 (200)

Grafting for Combinatorial Boolean Model using Frequent Itemset Mining

, 2017
This paper introduces the combinatorial Boolean model (CBM), which is defined as the class of linear combinations of conjunctions of Boolean attributes. This paper addresses the issue of learning CBM from labeled data.
Lee, Taito, Matsushima, Shin, Yamanishi, Kenji   +2 more
core  

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, Cao L, Gao Y, Luo H, Meng X, Wang Y, Li J, Liu W.   +7 more
europepmc   +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

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

TKFIM: Top-K frequent itemset mining technique based on equivalence classes. [PDF]

PeerJ Comput Sci, 2021
Iqbal S, Shahid A, Roman M, Khan Z, Al-Otaibi S, Yu L.   +5 more
europepmc   +1 more source

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, Lu Yang, Philippe Fournier-Viger, Jaroslav Frnda, Lukas Sevcik, Miroslav Voznak   +5 more
doaj   +1 more source

Frequent Closed High-Utility Itemset Mining Algorithm Based on Leiden Community Detection and Compact Genetic Algorithm

IEEE Access
Traditional 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, Muhammad Asif Habib, Muhammad Ashraf, Shahzad Sarwar, Muhammad Umar Chaudhry, Hamayoun Shahwani, Mudassar Ahmad, Ch. Muhammad Nadeem Faisal   +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, Arias Vicente, Marta   +1 more
openaire   +3 more sources