Non-hierarchical Clustering using k-Means and EM Algorithms

• 발행기관: 한국유통과학회
• 저자명: Yong-Gyu Jung Jun Heo Kyu-Ho Kim
• 간행물 정보: 『한국유통과학회 학술대회 논문집』2013년 동계 국제학술대회, 277~280쪽, 전체 4쪽
• 주제분류: 경제경영 > 경제학
• 파일형태: PDF
• 발행일자: 2013.12.11

영문 초록

Purpose - The data mining techniques are applied in various fields as a method to extract information based on massive data utilized as useful modeling techniques. However, it is a difficult problem to present the perfect result while considering all circumstances if applied in actual problems, as multiple variables that affects the result exists. Research design, data, methodology - In this paper, non-hierarchical clustering algorithms of k-means and EM are compared through experiments. Clustering is given observations of similar things grouped together. It is characterized by identifying the structure of the entire data analysis method. The entire data into several clusters is able to isolate the technique of maximum purpose. Results - Comparing two algorithms, experimental data are described with a brief introduction to clustering techniques for k-means and EM algorithm for the description. Conclusions - With consideration from the experimental results, experimental method was proposed in the two algorithms for comparison with the results for the experiments

목차

Abstract

1. Introduction

2. Literature Review

3. Experimental Data Features

4. Experimental Results

5. Conclusion

References

키워드

k-means EM Apriori algorithm association analysis