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논문 기본 정보
- 자료유형
- 학위논문
- 저자정보
- 지도교수
- 임동훈
- 발행연도
- 2016
- 저작권
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Data clustering is a common technique used in data analysis and is used in many applications, such as artificial intelligence, pattern recognition, economics, ecology, psychiatry and marketing. K-Means clustering is a well-known clustering algorithm aiming to cluster a set of data points to a predefined number of clusters.
In this paper, we implemented K-Means clustering algorithm based on MapReduce framework with RHadoop to make the clustering method applicable to large scale data. RHadoop is a collection of R packages that allow users to manage and analyze data with Hadoop. Our main idea introduced a combiner as a function of our map output in order to decrease the amount of data needed to be processed by reducers. We also implemented RS(R-squared) and Elbow methods with MapReduce for finding the optimum number of clusters for K-Means clustering on large dataset.
The experimental results demonstrated that K-Means clustering algorithm using RHadoop can scale well and efficiently process large data sets on commodity hardware. Comparison with MapReduce implementation of RS, Elbow methods and classical K-Means clustering processing on small data showed similar results. We also showed that our K-Means clustering algorithm using RHadoop with combiner was more faster than regular algorithm without combiner as the size of data set increases.
In this paper, we implemented K-Means clustering algorithm based on MapReduce framework with RHadoop to make the clustering method applicable to large scale data. RHadoop is a collection of R packages that allow users to manage and analyze data with Hadoop. Our main idea introduced a combiner as a function of our map output in order to decrease the amount of data needed to be processed by reducers. We also implemented RS(R-squared) and Elbow methods with MapReduce for finding the optimum number of clusters for K-Means clustering on large dataset.
The experimental results demonstrated that K-Means clustering algorithm using RHadoop can scale well and efficiently process large data sets on commodity hardware. Comparison with MapReduce implementation of RS, Elbow methods and classical K-Means clustering processing on small data showed similar results. We also showed that our K-Means clustering algorithm using RHadoop with combiner was more faster than regular algorithm without combiner as the size of data set increases.
목차
Ⅰ. 서론 1Ⅱ. RHadoop 플랫폼 31. R 32. Hadoop 33. RHadoop 61) 개요 62) MapReduce 7Ⅲ. RHadoop 플랫폼에서 K-평균 클러스터링 성능 실험 및 분석 101. K-평균 클러스터링 개요 102. 실험환경 113. 실험데이터 131) 실제 데이터 132) 모의실험 데이터 144. K-평균 클러스터링 알고리즘 구현 155. 최적의 군집수 결정 201) 군집수 결정방법 202) 군집수 결정방법 알고리즘 구현 223) 실제 데이터 254) 모의실험 데이터 296. 컴바이너 사용여부에 따른 RHadoop 성능 비교 321) 실제 데이터 322) 모의실험 데이터 35Ⅳ. 결론 및 향후 연구 38참고문헌 39
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