Results 71 to 80 of about 2,506 (215)
Scalable frequent itemset mining on many-core processors
, 2022 Frequent-itemset mining is an essential part of the association rule mining process, which has many application areas. It is a computation and memory intensive task with many opportunities for optimization.
Schlegel, Benjamin +3 more
core +1 more source
Frequent Itemset Mining for Big Data Using Greatest Common Divisor Technique
Data Science Journal, 2017 The discovery of frequent itemsets is one of the very important topics in data mining. Frequent itemset discovery techniques help in generating qualitative knowledge which gives business insight and helps the decision makers. In the Big Data era the need
Mohamed A. Gawwad +2 more
doaj +1 more source
IET Generation, Transmission &Distribution, Volume 18, Issue 13, Page 2313-2324, July 2024.This paper proposes an electric vehicle (EV) load diagnosis algorithm considering data privacy. The validity of the algorithm in this paper is verified by using the real collected EV load data. Abstract Accurate forecasting of electric vehicle (EV) load is essential for grid stability and energy management. EV load forecasting is influenced by multiple
Ruien Bian +3 more
wiley +1 more source
Reconstructing thicket clump formation using association rules analysis
Journal of Vegetation Science, Volume 35, Issue 3, May/June 2024.Association rules (or market basket) analysis was effective in eliciting common associations between species and size classes across different stages of thicket clump formation in a savanna. Vachellia karroo established alone in open grassland, whereas a suite of clump‐initiating species recruited in close association with large V.
Rhys Nell +2 more
wiley +1 more source
Frequent Itemset Mining in Large Datasets a Survey
, 2017 Frequent Itemset Mining is a well-known area in data mining. Most of the techniques available for frequent itemset mining requires complete information about the data which can result in generation of the association rules.
Manish Kumar, Amrit Pal
core +1 more source
IET Intelligent Transport Systems, Volume 18, Issue 3, Page 517-527, March 2024.Abstract This study aims to explore the relationship between traffic flow states and crash type/severity in the scenarios of normal crashes, primary crashes, and secondary crashes using the association rules mining approach. The crash data and real‐time traffic data were collected from the I‐880 freeway for five years in California, USA.
Bo Yang +4 more
wiley +1 more source
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
Frequent itemset mining on multiprocessor systems.
, 2014 Frequent itemset mining is an important building block in many data mining applications like market basket analysis, recommendation, web-mining, fraud detection, and gene expression analysis. In many of them, the datasets being mined can easily grow up to hundreds of gigabytes or even terabytes of data.
openaire +3 more sources
Efficiently mining maximal frequent itemsets
Proceedings 2001 IEEE International Conference on Data Mining, 2002 We present GenMax, a backtracking search based algorithm for mining maximal frequent itemsets. GenMax uses a number of optimizations to prune the search space. It uses a novel technique called progressive focusing to perform maximality checking, and diffset propagation to perform fast frequency computation.
Karam Gouda, Mohammed Javeed Zaki
openaire +1 more source