Abstract

Adaptive semi-supervised affinity propagation clustering algorithm based on structural similarity

Highlights

  • Affinity Propagation (AP), proposed by Frey and Dueck, is a fast and efficient clustering algorithm

  • American scholars Givoni I. et al extended AP in a principled way to solve the hierarchical clustering problem and proposed Hierarchical Affinity Propagation, which was successfully applied to actual HIV genetic sequence data [11]

  • We propose a novel adaptive semi-supervised affinity propagation clustering algorithm based on structural similarity (SAAP-SS) in this paper

Read more

Summary

Introduction

Affinity Propagation (AP), proposed by Frey and Dueck, is a fast and efficient clustering algorithm This distinctive clustering algorithm does not require the number of clusters to be predetermined like other clustering algorithms do, instead it considers all data points as potential exemplars and finds the optimal ones through continuous iteration [1]. It is widely used in gene sequence analysis [2], text clustering [3], image processing [4, 5], facility location [6] and many other fields [7÷9]. Wang Xianhui et al combined AP with K-means clustering and put forward an AP-based cluster ensemble algorithm that effectively improves the accuracy, robustness and stability of K-means clustering [13]

Objectives
Results
Conclusion
Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.