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SOM-ELM—Self-Organized Clustering using ELM
- Source :
- Neurocomputing. 165:238-254
- Publication Year :
- 2015
- Publisher :
- Elsevier BV, 2015.
-
Abstract
- This paper presents two new clustering techniques based on Extreme Learning Machine (ELM). These clustering techniques can incorporate a priori knowledge (of an expert) to define the optimal structure for the clusters, i.e. the number of points in each cluster. Using ELM, the first proposed clustering problem formulation can be rewritten as a Traveling Salesman Problem and solved by a heuristic optimization method. The second proposed clustering problem formulation includes both a priori knowledge and a self-organization based on a predefined map (or string). The clustering methods are successfully tested on 5 toy examples and 2 real datasets.
- Subjects :
- ta113
Clustering high-dimensional data
Fuzzy clustering
Cognitive Neuroscience
Correlation clustering
Single-linkage clustering
Constrained clustering
computer.software_genre
Computer Science Applications
Data stream clustering
Artificial Intelligence
CURE data clustering algorithm
Machine learning
Data mining
Cluster analysis
computer
Mathematics
Subjects
Details
- ISSN :
- 09252312
- Volume :
- 165
- Database :
- OpenAIRE
- Journal :
- Neurocomputing
- Accession number :
- edsair.doi.dedup.....61c056a3b1740f063a1eda5b7f1b8d3b
- Full Text :
- https://doi.org/10.1016/j.neucom.2015.03.014