원문정보
보안공학연구지원센터(IJDTA)
International Journal of Database Theory and Application
Vol.8 No.1
2015.02
pp.87-94
피인용수 : 0건 (자료제공 : 네이버학술정보)
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
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
저자정보
참고문헌
자료제공 : 네이버학술정보