Title :
An improved Single-Pass clustering algorithm internet-oriented network topic detection
Author :
Yi Xiaolin ; Zhao Xiao ; Ke Nan ; Zhao Fengchao
Author_Institution :
Coll. of Comput. Sci., Beijing Univ. of Technol., Beijing, China
Abstract :
The Single-Pass clustering algorithm, its two main disadvantages are easily affected by the orders of inputs of text and low precision when we use it to process the network text clustering. Through introducing the concept of seeds of topic, the paper proposed an improved Single-Pass clustering algorithm which inherited the main means of Single-Pass clustering algorithm. The experiment results showed that the improved algorithm could not only improve the speed of clustering, but also decrease the probabilities of miss detection, false detection, and the cost of wrong detection. The improved Single-Pass clustering algorithm that has improved the quality of clustering and topic detection both has high practicability and good reference value to the research of analysis for internet public opinion.
Keywords :
Internet; cognition; information retrieval; pattern clustering; text detection; Internet oriented network topic detection; Internet public opinion analysis; false detection probability; miss detection probability; network text clustering; single pass clustering algorithm; Algorithm design and analysis; Clustering algorithms; Computer science; Educational institutions; Heuristic algorithms; Internet; Vectors; incremental clustering; nearest neighbor-clustering; text clustering; topic detection and tracking;
Conference_Titel :
Intelligent Control and Information Processing (ICICIP), 2013 Fourth International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4673-6248-1
DOI :
10.1109/ICICIP.2013.6568138