@article {10.3844/jcssp.2018.1213.1225,
article_type = {journal},
title = {A Reformed K-Nearest Neighbors Algorithm for Big Data Sets},
author = {Phu, Vo Ngoc and Ngoc Tran, Vo Thi},
volume = {14},
number = {9},
year = {2018},
month = {Mar},
pages = {1213-1225},
doi = {10.3844/jcssp.2018.1213.1225},
url = {https://thescipub.com/abstract/jcssp.2018.1213.1225},
abstract = {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).},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}