Name
Abstract | ||
|---|---|---|
Humongous amounts of data have created a lot of challenges in terms of data computation and analysis. Classic data mining techniques are not prepared for the new space and time requirements. Discretization and dimensionality reduction are two of the data reduction tasks in knowledge discovery. Random Projection Random Discretization is a novel and recently proposed ensemble method by Ahmad and Brown in 2014 that performs discretization and dimensionality reduction to create more informative data. Despite the good efficiency of random projections in dimensionality reduction, more robust methods like Principal Components Analysis (PCA) can improve the performance. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.knosys.2018.03.012 | Knowledge-Based Systems |
Keywords | Field | DocType |
Big Data,Discretization,Spark,Decision tree,PCA,Data reduction. | Random projection,Data mining,Discretization,Dimensionality reduction,Computer science,Curse of dimensionality,Knowledge extraction,Random forest,Principal component analysis,Data reduction | Journal |
Volume | Issue | ISSN |
150 | C | 0950-7051 |
Citations | PageRank | References |
6 | 0.45 | 18 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Diego García-Gil | 1 | 19 | 2.69 |
| Sergio Ramírez-Gallego | 2 | 98 | 6.99 |
| Salvador García | 3 | 4151 | 118.45 |
| Francisco Herrera | 4 | 27391 | 1168.49 |