Memberships Networks for High-Dimensional Fuzzy Clustering Visualization
Visualizing the cluster structure of high-dimensional data is a non-trivial task that must be able to deal with the large dimensionality of the input data. Unlike hard clustering structures, visualization of fuzzy clusterings is not as straightforward because soft clustering algorithms yield more co...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2019
- Institución:
- Universidad de Medellín
- Repositorio:
- Repositorio UDEM
- Idioma:
- eng
- OAI Identifier:
- oai:repository.udem.edu.co:11407/5662
- Acceso en línea:
- http://hdl.handle.net/11407/5662
- Palabra clave:
- Clustering visualization
Fuzzy clustering
High-dimensional data
Membership network
Cluster analysis
Complex networks
Data visualization
Fuzzy clustering
Input output programs
Large dataset
Visualization
Cluster structure
Financial profiles
Hard clustering
High dimensional data
High-dimensional
Non-trivial tasks
Simple networks
Weighted networks
Clustering algorithms
- Rights
- License
- http://purl.org/coar/access_right/c_16ec
Summary: | Visualizing the cluster structure of high-dimensional data is a non-trivial task that must be able to deal with the large dimensionality of the input data. Unlike hard clustering structures, visualization of fuzzy clusterings is not as straightforward because soft clustering algorithms yield more complex clustering structures. Here is introduced the concept of membership networks, an undirected weighted network constructed based on the fuzzy partition matrix that represents a fuzzy clustering. This simple network-based method allows understanding visually how elements involved in this kind of complex data clustering structures interact with each other, without relying on a visualization of the input data themselves. Experiment results demonstrated the usefulness of the proposed method for the exploration and analysis of clustering structures on the Iris flower data set and two large and unlabeled financial datasets, which describes the financial profile of customers of a local bank. © 2019, Springer Nature Switzerland AG. |
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