Now showing items 1-10 of 1131
Decision Tree based Classifiers for Large Datasets
(Computación y Sistemas; Vol. 17 No. 1, 3013-03-06)
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 ...
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 ...
Efficient supervised optimum-path forest classification for large datasets
(Elsevier B.V., 2012-01-01)
Today data acquisition technologies come up with large datasets with millions of samples for statistical analysis. This creates a tremendous challenge for pattern recognition techniques, which need to be more efficient ...
Improving the accuracy of the optimum-path forest supervised classifier for large datasets
In this work, a new approach for supervised pattern recognition is presented which improves the learning algorithm of the Optimum-Path Forest classifier (OPF), centered on detection and elimination of outliers in the ...
Computing sparse representations of multidimensional signals using Kronecker bases
(M I T Press, 2013-01)
Recently, there is a great interest in sparse representations of signals under the assumption that signals (datasets) can be well approximated by a linear combination of few elements of a known basis (dictionary). Many ...