Advertisement
Published in:
2018 | OriginalPaper | Chapter
A Target Dominant Sets Clustering Algorithm
Authors : Jian Hou, Chengcong Lv, Aihua Zhang, Xu E.
Published in: Artificial Neural Networks and Machine Learning – ICANN 2018
Publisher: Springer International Publishing
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
Abstract
The dominant sets clustering algorithm has some interesting properties and has achieved impressive results in experiments. However, with the data represented as feature vectors, we need to estimate data similarity and the regularization parameter influences the clustering results and number of clusters significantly. To obtain a specified number of clusters efficiently with the dominant sets algorithm, we present a target dominant set clustering algorithm. Our algorithm detects clusters in the first step, and then extracts dominant sets around the cluster centers based on a specially designed game dynamics. In addition, we show that this game dynamics can be utilized to reduce the computation and memory load significantly. Experiments show that our algorithm performs favorably to the original dominant sets algorithm in clustering quality with much smaller computation load than the latter.