Fast k-nearest neighbors for Big Data and Smart Data
- Maillo Hidalgo, Jesús
- Francisco Herrera Triguero Director
- Isaac Triguero Velázquez Director
Universidade de defensa: Universidad de Granada
Fecha de defensa: 07 de maio de 2020
- Óscar Cordón García Presidente/a
- Victoria Luzón García Secretario/a
- Antonio Peregrín Rubio Vogal
- Daniel Peralta Cámara Vogal
- Javier del Ser Lorente Vogal
Tipo: Tese
Resumo
In this thesis, we have presented an extensive study of the kNN algorithm in Big Data problems and its application to transform Big Data into Smart Data. The objective has been to the design, implementation, analysis and evaluation of the proposed algorithms. This thesis started by enabling the original kNN classifier to tackle Big Data problems, and then we extended that proposal to allow its fuzzy variation, in order to improve the scalability and accuracy. Afterwards, the implication of the kNN algorithm in obtaining Smart Data is analysed, highlighting the proposal as an imputation of MVs. Finally, two specific complexity and density metrics for Big Data problems are proposed in order to study the redundancy information in large scale datasets.