A novel algorithm DBCAPSIC for clustering non-numeric data

作者：Geng, Jinkun; Ye, Daren; Luo, Ping

来源：IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2015, Chongqing, China, 2015-12-19 To 2015-12-20.

DOI：10.1109/IAEAC.2015.7428564

• DBCAPSIC
• Density-based algorithm
• Density-based clustering algorithms
• K-means
• Linear time complexity
• Network information
• sensitivity
• Sensitivity to initial conditions

Data mining techniques are playing an important role in the analysis of mass network information and big data nowadays. The cluster analysis, as a main kind of method in data mining, draws great interest from researchers of various fields who proposed many algorithms such as k-means algorithm and its variants, density-based algorithm and its variants. However, these algorithms all have their own problems. This paper focuses on some of the problems and proposes a novel algorithm DBCAPSIC. The algorithm overcomes the k-means algorithm's sensitivity to initial conditions and avoids common density-based algorithms'"clustering failure"in some cases. Also, the algorithm has the linear time complexity of O{n), compared to the quadratic time complexity of common density-based clustering algorithms.

• 出版日期2015
• 单位清华大学