|
ISSN
| 1549-3636 | |
DDC
| 004 | |
Tác giả CN
| Vo, Ngoc Phu | |
Nhan đề
| A reformed K-Nearest neighbors algorithm for big data sets / Vo Ngoc Phu, Vo Thi Ngoc Tran | |
Thông tin xuất bản
| New York : Science Publications, 2018 | |
Mô tả vật lý
| 13 p. | |
Tóm tắt
| 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). | |
Thuật ngữ chủ đề
| Big Data | |
Từ khóa tự do
| Distributed System | |
Từ khóa tự do
| Algorithm | |
Từ khóa tự do
| K-Nearest Neighbors | |
Từ khóa tự do
| K-NN | |
Từ khóa tự do
| Parallel Network Environment | |
Từ khóa tự do
| Data Mining | |
Từ khóa tự do
| Cloudera | |
Từ khóa tự do
| Association Rules | |
Từ khóa tự do
| Hadoop Map | |
Từ khóa tự do
| Hadoop Reduce | |
Khoa
| Khoa Công nghệ Thông tin | |
Tác giả(bs) CN
| Vo, Thi Ngoc Tran | |
Nguồn trích
| Journal of Computer Science.
Số: Vol. 14, Issue 9, P.1213-1225, , | |
Địa chỉ
| Thư Viện Đại học Nguyễn Tất Thành | |
Tệp tin điện tử
| doi.org/10.3844/jcssp.2018.1213.1225 |
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| 100 | |aVo, Ngoc Phu |
|---|
| 245 | |aA reformed K-Nearest neighbors algorithm for big data sets / |cVo Ngoc Phu, Vo Thi Ngoc Tran |
|---|
| 260 | |aNew York : |bScience Publications, |c2018 |
|---|
| 300 | |a13 p. |
|---|
| 520 | |aA 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). |
|---|
| 650 | |aBig Data |
|---|
| 653 | |aDistributed System |
|---|
| 653 | |aAlgorithm |
|---|
| 653 | |aK-Nearest Neighbors |
|---|
| 653 | |aK-NN |
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| 653 | |aParallel Network Environment |
|---|
| 653 | |aData Mining |
|---|
| 653 | |aCloudera |
|---|
| 653 | |aAssociation Rules |
|---|
| 653 | |aHadoop Map |
|---|
| 653 | |aHadoop Reduce |
|---|
| 690 | |aKhoa Công nghệ Thông tin |
|---|
| 700 | |aVo, Thi Ngoc Tran |
|---|
| 773 | |tJournal of Computer Science|gVol. 14, Issue 9, P.1213-1225 |
|---|
| 852 | |aThư Viện Đại học Nguyễn Tất Thành |
|---|
| 856 | |udoi.org/10.3844/jcssp.2018.1213.1225 |
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| 890 | |c1|a0|b0|d2 |
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