Initial Free Self-Organizing Map by Aggregation
Year
: 2013
Abstract: Initialization parameters of Self-organizing map networks (SOM) have a great influence in
their final result. This is the only known problem of SOM networks. The uncertainty of
solution is resolved with the fusion methods in many machine learning algorithms. Motivated
by this application, we aim to reduce the uncertainty in SOM final result and provide a more
robust solution. This is done by aggregating the results obtained from several run of SOM
network. The objective in uniting several SOM run is to reduce the effect of two above factors
and reach a more appropriate solution. To achieve this, a number of methods for integration
of SOM solutions have been proposed and their characteristics are discussed in detail.
Finally, these approaches are experimentally compared with some recently proposed SOM
networks and their effectiveness are investigated.
their final result. This is the only known problem of SOM networks. The uncertainty of
solution is resolved with the fusion methods in many machine learning algorithms. Motivated
by this application, we aim to reduce the uncertainty in SOM final result and provide a more
robust solution. This is done by aggregating the results obtained from several run of SOM
network. The objective in uniting several SOM run is to reduce the effect of two above factors
and reach a more appropriate solution. To achieve this, a number of methods for integration
of SOM solutions have been proposed and their characteristics are discussed in detail.
Finally, these approaches are experimentally compared with some recently proposed SOM
networks and their effectiveness are investigated.
Keyword(s): Self-Organizing Map,Clustering Fusion,Initial Network,Data Sequence,Robustness,Cluster Validity
Collections
:
-
Statistics
Initial Free Self-Organizing Map by Aggregation
Show full item record
| contributor author | امین اله یار | en |
| contributor author | هادی صدوقی یزدی | en |
| contributor author | Amin Allahyar | fa |
| contributor author | Hadi Sadoghi Yazdi | fa |
| date accessioned | 2020-06-06T13:12:43Z | |
| date available | 2020-06-06T13:12:43Z | |
| date issued | 2013 | |
| identifier uri | https://libsearch.um.ac.ir:443/fum/handle/fum/3346052 | |
| description abstract | Initialization parameters of Self-organizing map networks (SOM) have a great influence in their final result. This is the only known problem of SOM networks. The uncertainty of solution is resolved with the fusion methods in many machine learning algorithms. Motivated by this application, we aim to reduce the uncertainty in SOM final result and provide a more robust solution. This is done by aggregating the results obtained from several run of SOM network. The objective in uniting several SOM run is to reduce the effect of two above factors and reach a more appropriate solution. To achieve this, a number of methods for integration of SOM solutions have been proposed and their characteristics are discussed in detail. Finally, these approaches are experimentally compared with some recently proposed SOM networks and their effectiveness are investigated. | en |
| language | English | |
| title | Initial Free Self-Organizing Map by Aggregation | en |
| type | Journal Paper | |
| contenttype | External Fulltext | |
| subject keywords | Self-Organizing Map | en |
| subject keywords | Clustering Fusion | en |
| subject keywords | Initial Network | en |
| subject keywords | Data Sequence | en |
| subject keywords | Robustness | en |
| subject keywords | Cluster Validity | en |
| journal title | International Journal of Control and Automation | fa |
| pages | 81-98 | |
| journal volume | 6 | |
| journal issue | 2 | |
| identifier link | https://profdoc.um.ac.ir/paper-abstract-1034407.html | |
| identifier articleid | 1034407 |