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Métodos para mejorar la calidad de un conjunto de datos para descubrir conocimiento
(2007)
Today, data generation is growing exponentially in both directions; instances (rows) and features (columns). This causes that many datasets can not be analyzed without preprocessing. The large size of the dataset to be ...
On applications of rough sets theory to knowledge discovery
(2007)
Knowledge Discovery in Databases (KDD) is the nontrivial extraction of implicit, previously unknown and potentially useful information from data. Data preprocessing is a step of the KDD process that reduces the complexity ...
Contributions to parallel and distributed computing in knowledge discovery and data mining
(2006)
Recently databases are increasing continuously without bound, due to new data acquisition technologies. One challenge is how to gain knowledge from these large data sets. In this thesis, we analyze and improve the algorithmic ...
Generalizaciones de minimos cuadrados parciales con aplicación en clasificacion supervisada
(2004)
The development of technologies such as microarrays has generated a large amount of data. The main characteristic of this kind of data it is the large number of predictors (genes) and few observations (experiments). Thus, ...
Semi-supervised document classification using ontologies
(2011)
Many modern applications of automatic document classification require learning accurately with little training data. Addressing the need to reduce the manual labeling process, the semi-supervised classification technique ...
Temporal outlier detection using dynamic Bayesian networks and probabilistic association rules
(2019-12-10)
Temporal datasets provide records of the evolution and dependencies of random variables over time. Recently, there has been an increase in the application of temporal datasets in areas such as intrusion detection, fraud ...