Earticle

초록

영어

The traditional clustering algorithms need some prior knowledge to determine the initial parameters, and the selection of initial parameters usually will produce great influence on the result of clustering. In order to achieve this purpose, we put forward a new clustering algorithm based on "idea", through optimizing the given data and mathematical model between the adaptive data to model the best match. This algorithm combined with an "idea" algorithm to learn the hybrid model because of the gaussian mixture model can be thought of as a distribution of soft clustering method. This clustering algorithm can overcome the shortcomings of k-means algorithm. Experimental result shows that the algorithm has better clustering effect.

목차

Abstract
 1. Introduction
 2. Research Review of Graph Clustering Technology
 3. General Idea of Wax
 4. Clustering Algorithm Based on Wax
  4.1. Wax Hierarchical Clustering Method
  4.2. Application range of Wax
 5. Simulation Experiments
  5.1. Simulation Data Analysis
  5.2. Application Data Analysis
 6. Conclusions
 References

키워드

저자

• Zhang Jianyan [ School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang City, China ]
• Zheng Kun [ School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang City, China ]
• Li Tianliang [ China Mobile Communications Corporation, Hebei Mobile Communication Co. Ltd, Shijiazhuang City, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605