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

被引：1

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

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