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Decision Tree based Classifiers for Large Datasets
(Computación y Sistemas; Vol. 17 No. 1, 2013-03-06)
Abstract: In this paper, several algorithms have been developed for building decision trees from large datasets. These algorithms overcome some restrictions of the most recent algorithms in the state of the art. Three of ...
Parallel Algorithm for Reduction of Data Processing Time in Big Data
(Institute of Physics Publishing, 2020-01-07)
Technological advances have allowed to collect and store large volumes of data over the years. Besides, it is significant that today's applications have high performance and can analyze these large datasets effectively. ...
Decision tree induction using a fast splitting attribute selection for large datasets
(Elsevier Ltd., 2011)
A fast and low-cost method to detect nearduplicate Images in large dataset based on fingerprint extraction and Deep Learning
(Biblioteca Digital wdg.biblioUniversidad de Guadalajara, 2023-03-16)
Recognizing near-duplicate images from large datasets is a crucial task in image retrieval
and content identification. Finding similar images in order to reduce redundancy is timeconsuming in large datasets. Most of image ...
Distance-based clustering methods for large datasets
(Universidade Federal de Minas GeraisUFMG, 2018-07-30)
This PhD dissertation presents a methodology focused on clustering problems with large data volumes. The goal is to design algorithms that can process large volumes of data without loss of clustering quality. Specifically, ...
ClusMAM: fast and effective unsupervised clustering of large complex datasets using metric access methods
(Association for Computing Machinery - ACMUniversity of PisaScuola Superiore Sant’AnnaPisa, 2016-04)
An efficient and effective clustering process is a core task of data mining analysis, and has become more important in the nowadays scenario of big data, where scalability is an issue. In this paper we present the ClusMAM ...