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Clustering by defining and merging candidates of cluster centers via independence and affinity
- Source :
- Neurocomputing. 315:486-495
- Publication Year :
- 2018
- Publisher :
- Elsevier BV, 2018.
-
Abstract
- Clustering analysis is to classify elements into categories based on their similarity. Clustering by fast search and find of density peaks (CFSFDP) has been proven to be an effective and novel algorithm, which identifies the centers of clusters with density maxima. However, the performance of CFSFDP is quite sensitive to the estimation of densities, that is exactly the selection of the cutoff distance (dc). In a conventional way, the selection of dc is based on subjective experience. It meets difficulties in finding an appropriate dc, especially for detecting nonspherical clusters, because CFSFDP cannot perform well when there are more than one density peak for one cluster. Besides, another barrier of applying CFSFDP is that manual interaction is always required for making an effective selection of cluster centers. In this paper, a new density-based clustering algorithm, clustering by defining and merging candidates of cluster centers via independence and affinity (CDMC-IA), is proposed. With its strategy, an appropriate value of cutoff distance dc can be well suggested and the robustness of the method itself is enhanced. Moreover, CDMC-IA introduces a new quantity independence to sort and select cluster centers, instead of human based selection from decision graph. Another quantity affinity is also introduced, which well handles multiple density peaks existing in one cluster and is able to assign each data point to the its targeted cluster. The performance of applying conventional clustering methods to benchmark datasets will be compared with the proposed method in this paper.
- Subjects :
- 0301 basic medicine
business.industry
Computer science
Cognitive Neuroscience
Kernel density estimation
Pattern recognition
02 engineering and technology
Computer Science Applications
03 medical and health sciences
030104 developmental biology
Artificial Intelligence
Robustness (computer science)
0202 electrical engineering, electronic engineering, information engineering
Cluster (physics)
020201 artificial intelligence & image processing
Artificial intelligence
Cluster analysis
business
Subjects
Details
- ISSN :
- 09252312
- Volume :
- 315
- Database :
- OpenAIRE
- Journal :
- Neurocomputing
- Accession number :
- edsair.doi...........fd240da07b0874d18fe4a9f608be9243