A distributed approach for high-dimensionality heterogeneous data reduction
The recent explosion of data size in number of records and attributes has triggered the
development of a number of Big Data analytics as well as parallel data processing methods …
development of a number of Big Data analytics as well as parallel data processing methods …
The Main Big Data Solution Pillars: How to Effectively Model and Manage the Massive Data Deluge?
RM Gahar, M Sassi Hidri - Journal of Information & Knowledge …, 2024 - World Scientific
In today's data-driven world, the volume of information produced daily is staggering. Without
a robust data engineering strategy, companies face the risk of prolonged delays, decreased …
a robust data engineering strategy, companies face the risk of prolonged delays, decreased …
An ontology-driven mapreduce framework for association rules mining in massive data
To be competitive, companies need to be able to take advantage of the huge amounts of
data, called also Big Data deluge, to predict what might happen in the future. In this way …
data, called also Big Data deluge, to predict what might happen in the future. In this way …
A review on Ensemble learning based maximal frequent pattern mining over Cloud
GR Anil, JSP Peter, BD Jitkar - 2024 IEEE International …, 2024 - ieeexplore.ieee.org
In digital era vast quantity of data is produced by smart machines and it is challenging work
to store and evaluate the Big data. The frequent pattern mining in Big data plays extremely …
to store and evaluate the Big data. The frequent pattern mining in Big data plays extremely …
STARM: STreaming Association Rules Mining in High-Dimensional Data
Predictive analytics involves using Data Mining algorithms to discover knowledge from large
databases. The Association Rules (ARs) mining technique is considered to be one of the …
databases. The Association Rules (ARs) mining technique is considered to be one of the …
A Distributed SAT-Based Framework for Closed Frequent Itemset Mining
Abstract Frequent Itemset Mining is an essential part of data mining. SAT-based approaches
that extract frequent itemsets in big data encounter significant challenges when computing …
that extract frequent itemsets in big data encounter significant challenges when computing …
Framework of EcomTDMA for Transactional Data Mining Using Frequent Item Set for E-Commerce Application
In data mining, comprehending out the common item set is an indispensable job. In
statements such as participation rule mining and co-relationships, these conventional item …
statements such as participation rule mining and co-relationships, these conventional item …