Results 81 to 90 of about 2,690 (229)

ENHANCED ALGORITHMS FOR MINING OPTIMIZED POSITIVE AND NEGATIVE ASSOCIATION RULE FROM CANCER DATASET

ICTACT Journal on Soft Computing, 2018
The most important research aspect nowadays is the data. Association rule mining is vital mining used in data which mines many eventual informations and associations from enormous databases.
I Berin Jeba Jingle, J Jeya ACelin
doaj   +1 more source

A Robust Technique for Closed Frequent and High Utility Itemsets Mining: Closed-FHUIM

IEEE Access
Frequent itemset mining (FIM) and high utility itemset mining (HUIM) are popular data mining techniques used in various real-world applications such as retail-market, bio-medicine, and click-stream analysis.
Muhammad Waheed Ashraf, M. Asif Naeem, Heejeong Jasmine Lee   +2 more
doaj   +1 more source

Electric vehicle load forecasting based on convolutional networks with attention mechanism and federated learning method

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, Long Wang, Yadong Liu, Zhou Dai   +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, Tiffany Pillay, Susanne Vetter   +2 more
wiley   +1 more source

Mining Frequent Itemsets for Evolving Database Involving Insertion

, 2015
Mining frequent itemsets is one of the popular task in data mining. There are many applications like location-based services, sensor monitoring systems, and data integration in which the content of transaction is uncertain in nature.
, Mrs. Ashlesha A. Jagdale, Prof. Sonali Patil
core   +1 more source

Exploring the impacts of traffic flow states on freeway normal crashes, primary crashes, and secondary crashes

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, Yanyong Guo, Weihua Zhang, Ying Yao, Yiping Wu   +4 more
wiley   +1 more source

Mining frequent itemsets using the N-list and subsume concepts

, 2016
Frequent itemset mining is a fundamental element with respect to many data mining problems directed at finding interesting patterns in data. Recently the PrePost algorithm, a new algorithm for mining frequent itemsets based on the idea of N-lists, which ...
Coenen, F, Vo, B, Le, T, Hong, TP
core   +1 more source

Efficiently mining frequent itemsets from very large databases [PDF]

, 2004
Efficient algorithms for mining frequent itemsets are crucial for mining association rules and for other data mining tasks. Methods for mining frequent itemsets and for iceberg data cube computation have been implemented using a prefix-tree structure ...
Zhu, Jianfei
core  

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