A feature weighted affinity propagation clustering algorithm based on rough entropy reduction. Issue 1 (2019)

• Record Type: Journal Article
• Authors: Xu, Li
• Is Part Of: International journal of collaborative intelligence. Volume 2:Issue 1(2019)
• Page Start: 42
• Page End: 50
• Publication Date: 2019
• Subjects: rough entropy -- attribute reduction -- feature weighted -- normalisation -- AP clustering; Computational intelligence -- Periodicals; Cognitive science -- Periodicals; Information science -- Periodicals; 006.305
• Languages: English
• ISSNs: 2051-7122

Abstract

In the clustering task, each feature of data sample is not taken the same contribution, some features provides more related information to the final results, if they are treated equally with other features, not only the complexity of the algorithm is increased but also the accuracy of the final results will be affected. So as a key phase in clustering, feature weighting is becoming more and more concerned by scholars. This paper proposes a feature weighted affinity propagation clustering algorithm based on rough entropy reduction (FWRER-AP). Rough entropy is used to assign weights for every feature according to their different contribution. Then the optimisation samples are used in AP clustering algorithm, we can get the final clustering results through iterations. Compared with traditional AP clustering algorithm, experiment shows that the optimal algorithm not only reduces the complexity, but also improves the accuracy at the same time.