Results 31 to 40 of about 45,837 (241)
Finding the True Frequent Itemsets [PDF]
, 2013 Frequent Itemsets (FIs) mining is a fundamental primitive in data mining. It requires to identify all itemsets appearing in at least a fraction $\theta$ of a transactional dataset $\mathcal{D}$.
Riondato, Matteo, Vandin, Fabio
core +2 more sources
Finding Stable Periodic-Frequent Itemsets in Big Columnar Databases
IEEE Access, 2023 Stable periodic-frequent itemset mining is essential in big data analytics with many real-world applications. It involves extracting all itemsets exhibiting stable periodic behaviors in a temporal database.
Hong N. Dao +5 more
doaj +1 more source
Parallel Algorithm for Frequent Itemset Mining on Intel Many-core Systems [PDF]
, 2018 Frequent itemset mining leads to the discovery of associations and correlations among items in large transactional databases. Apriori is a classical frequent itemset mining algorithm, which employs iterative passes over database combining with generation
Zymbler, Mikhail
core +3 more sources
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
doaj
CPU Parallelization Eclat Algorithm Based on Bit Storage Tid [PDF]
Jisuanji gongcheng, 2018 The Eclat algorithm uses vertical data representation and does not require complex data structures.However,the intersection count generation mode causes a large amount of memory consumption and low mining efficiency in the process of mining frequent ...
SUN Zongxin,ZHANG Guiyun
doaj +1 more source
Frequent Itemsets Mining for Big Data: A Comparative Analysis
Big Data Research, 2017 Itemset mining is a well-known exploratory data mining technique used to discover interesting correlations hidden in a data collection. Since it supports different targeted analyses, it is profitably exploited in a wide range of different domains ...
D. Apiletti +5 more
semanticscholar +1 more source
Right-Hand Side Expanding Algorithm for Maximal Frequent Itemset Mining
Applied Sciences, 2021 When it comes to association rule mining, all frequent itemsets are first found, and then the confidence level of association rules is calculated through the support degree of frequent itemsets.
Yalong Zhang +4 more
doaj +1 more source
Video Mining with Frequent Itemset Configurations [PDF]
, 2006 We present a method for mining frequently occurring objects and scenes from videos. Object candidates are detected by finding recurring spatial arrangements of affine covariant regions. Our mining method is based on the class of frequent itemset mining algorithms, which have proven their efficiency in other domains, but have not been applied to video ...
Quack, Till +2 more
openaire +2 more sources
Frequent Itemset Mining and Association Rules [PDF]
, 2006 With the advent of mass storage devices, databases have become larger and larger. Point-of-sale data, patient medical data, scientific data, and credit card transactions are just a few sources of the ever-increasing amounts of data. These large datasets provide a rich source of useful information.
Imberman S., Tansel A.U.
openaire +3 more sources
Peak-Jumping Frequent Itemset Mining Algorithms [PDF]
, 2006 We analyze algorithms that, under the right circumstances, permit efficient mining for frequent itemsets in data with tall peaks (large frequent itemsets). We develop a family of level-by-level peak-jumping algorithms, and study them using a simple probability model. The analysis clarifies why the jumping idea sometimes works well, and which properties
Dexters, Nele +2 more
openaire +2 more sources