Fast k-nearest neighbors for Big Data and Smart Data

  1. Maillo Hidalgo, Jesús
Supervised by:
  1. Francisco Herrera Triguero Director
  2. Isaac Triguero Velázquez Director

Defence university: Universidad de Granada

Fecha de defensa: 07 May 2020

Committee:
  1. Óscar Cordón García Chair
  2. Victoria Luzón García Secretary
  3. Antonio Peregrín Rubio Committee member
  4. Daniel Peralta Cámara Committee member
  5. Javier del Ser Lorente Committee member

Type: Thesis

Teseo: 618774 DIALNET lock_openDIGIBUG editor

Abstract

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.