A Determination of an Optimal Clustering Method Based on Data Characteristics

• 영문명: A Determination of an Optimal Clustering Method Based on Data Characteristics
• 발행기관: 인문사회과학기술융합학회
• 저자명: Jeong-Hun Kim,Kwan-Hee Yoo,Aziz Nasridinov
• 간행물 정보: 『예술인문사회융합멀티미디어논문지』7권 8호, 305~314쪽, 전체 10쪽
• 주제분류: 사회과학 > 사회과학일반
• 파일형태: PDF
• 발행일자: 2017.08.31

영문 초록

Clustering is a method that collects data objects into groups based on their similary. Performance of the state-of-the-art clustering methods is different according to the data characteristics. There have been numerous studies that performed experiments to compare the accuracy of the state-of-the-art clustering methods by applying various kinds of datasets. A common problem of these studies is that they only consider clustering algorithms that yield the most accurate results for a particular dataset. They do not consider what factors affect the execution time of each clustering method and how they are affected. Nevertheless, execution time is an important factor in clustering performance if there is no significant difference in accuracy. In order to solve the problems of the existing research, through a series of experiments using various types of datasets, we compare the accuracy of four representative clustering methods. In addition, we perform practical clustering performance comparisons by deriving time complexity and identifying factors that influences to its performance.

목차

1. Introduction

2. Related Work

3. Proposed method

4. Experiment Results

5. Conclusion

References

키워드

Clustering,performance comparison,time complexity,precision,accuracy