Improved K-Means Clustering Algorithm Based on KD-Tree Approach

被引：3

作者：

Bhardwaj, Manish ^{[1
]}

Adane, Dattatraya ^{[2
]}

机构：

[1] Shri Ramdeobaba Coll Engn & Management, Comp Sci & Engn Dept, Nagpur, Maharashtra, India

[2] Shri Ramdeobaba Coll Engn & Management, Dept Informat Technol, Nagpur, Maharashtra, India

来源：

BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS | 2020年 / 13卷 / 14期

关键词：

K-MEANS; CLUSTERING ANALYSIS; OPENMP; PARALLEL APPROACH;

D O I：

10.21786/bbrc/13.14/38

中图分类号：

Q81 [生物工程学（生物技术）]; Q93 [微生物学];

学科分类号：

071005 ; 0836 ; 090102 ; 100705 ;

摘要：

The Cluster Analysis is a vast area of application such as security, Image recognition, scientific investigation, business intelligence, biology, and web search. K-Means clustering algorithm is not performing well with huge data sets in terms of Execution time. To overcome this, A Parallel Approach is used to implement the K-Means algorithm using OpenMP API with the KD-Tree approach to provide dynamic load balancing, optimized execution time, and maintaining accuracy. The experiments are performed on handwritten digits and Bagofword data sets by using a system with multi-core. After the analysis of the Sequential approach and Parallel approach of implementation of K-Means, it is observed that the parallel approach outperforms with similar accuracy utilizing the computing resources available with the multi-core systems.

引用

页码：160 / 163

页数：4

共 14 条

[1]

[Anonymous], 2015, IEEE SPONS 2 INT C I, DOI DOI 10.1109/ICIIECS.2015.71932

[2]

[Anonymous], 2009, INT C METH MOD COMP, DOI DOI 10.1109/ICM2CS.2009.5397976

[3]

Baydoun M, 2016, 2016 3RD INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTATIONAL TOOLS FOR ENGINEERING APPLICATIONS (ACTEA), P7, DOI 10.1109/ACTEA.2016.7560102

[4]

Chapman B., 2007, SCI ENG COMPUTATION

[5]

Di Fatta G., 2010, Proceedings of the 2010 IEEE 10th International Conference on Computer and Information Technology (CIT 2010), P2478, DOI 10.1109/CIT.2010.424

[6]

Han J, 2012, MOR KAUF D, P1

[7] k-means Performance Improvements with Centroid Calculation Heuristics both for Serial and Parallel environments [J].

Karimov, Jeyhun ;

Ozbayoglu, Murat ;

Dogdu, Erdogan .

2015 IEEE INTERNATIONAL CONGRESS ON BIG DATA - BIGDATA CONGRESS 2015, 2015, :444-451

[8]

Lecun Y., 2016, DATA SET HANDWRITTEN

[9] A Novel Density based Clustering Algorithm and Its Parallelization [J].

Li, Xiaokang ;

Yu, Binbin ;

Zhou, Yinghua ;

Sun, Guangzhong .

2014 15TH INTERNATIONAL CONFERENCE ON PARALLEL AND DISTRIBUTED COMPUTING, APPLICATIONS AND TECHNOLOGIES (PDCAT 2014), 2014, :1-6

[10]

Liao Q, 2013, 2013 15TH IEEE INTERNATIONAL CONFERENCE ON COMMUNICATION TECHNOLOGY (ICCT), P764, DOI 10.1109/ICCT.2013.6820477

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