TY  - JOUR
AU  - Phu, Vo Ngoc 
AU  - Ngoc Tran, Vo Thi 
PY  - 2018
TI  - A Reformed K-Nearest Neighbors Algorithm for Big Data Sets
JF  - Journal of Computer Science
VL  - 14
IS  - 9
DO  - 10.3844/jcssp.2018.1213.1225
UR  - https://thescipub.com/abstract/jcssp.2018.1213.1225
AB  - A Data Mining Has Already Had Many Algorithms Which A K-Nearest Neighbors Algorithm, K-NN, Is A Famous Algorithm For Researchers. K-NN Is Very Effective On Small Data Sets, However It Takes A Lot Of Time To Run On Big Datasets. Today, Data Sets Often Have Millions Of Data Records, Hence, It Is Difficult To Implement K-NN On Big Data. In This Research, We Propose An Improvement To K-NN To Process Big Datasets In A Shortened Execution Time. The Reformed K-Nearest Neighbors Algorithm (R-K-NN) Can Be Implemented On Large Datasets With Millions Or Even Billions Of Data Records. R-K-NN Is Tested On A Data Set With 500,000 Records. The Execution Time Of R-K-NN Is Much Shorter Than That Of K-NN. In Addition, R-K-NN Is Implemented In A Parallel Network System With Hadoop Map (M) And Hadoop Reduce (R).