Earticle

초록

영어

In traditional data mining process, the definition of mining objects and analysis tasks are all decided artificially based on the analysts’ knowledge and experience. To achieve intelligent data analysis, a method called thinking theme discovery technology is proposed to imitate humans’ thinking models. Since traditional thinking theme discovery algorithm is based on hierarchical clustering, the efficiency of which is far from acceptable with the increasing of data amounts. This paper improves the efficiency of the algorithm on density-based clustering method. With five complex network datasets and one commercial theme dataset, the experimental results show that both the effectiveness and efficiency of the algorithm are improved.

목차

Abstract
 1. Introduction
 2. Thinking Theme Discovery Algorithm Based on Hierarchical Clustering
  2.1. Basic Concepts
  2.2. Similarity Computation
  2.3. Algorithm Introduction
 3. Thinking Theme Discovery Algorithm on Density-Based Clustering
 4. Experimental Results Analysis
  4.1. Experimental Data and Environment
  4.2. Experimental Effectiveness Analysis
  4.3. Experimental Efficiency Analysis
 5. Conclusion
 References

키워드

저자

• Xuedong Gao [ University of Science and Technology Beijing, Beijing, P.R.China, 100083 ]
• Lei Zou [ University of Science and Technology Beijing, Beijing, P.R.China, 100083 ]
• Zengju Li [ University of Science and Technology Beijing, Beijing, P.R.China, 100083 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605