CSOM: self-organizing map for continuous data
Nowadays, lots of data is being collected for different industrial and commercial purposes, where the aim is to discover useful patterns from data which leads to discovery of valuable domain knowledge. Unsupervised learning is a useful method for these tasks as it requires no target class and it clu...
| Main Authors: | , |
|---|---|
| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IEEE
2005
|
| Online Access: | http://hdl.handle.net/20.500.11937/14636 |
| _version_ | 1848748675986096128 |
|---|---|
| author | Hadzic, Fedja Dillon, Tharam S. |
| author2 | Tharam Dillon |
| author_facet | Tharam Dillon Hadzic, Fedja Dillon, Tharam S. |
| author_sort | Hadzic, Fedja |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Nowadays, lots of data is being collected for different industrial and commercial purposes, where the aim is to discover useful patterns from data which leads to discovery of valuable domain knowledge. Unsupervised learning is a useful method for these tasks as it requires no target class and it clusters the feature values that occur frequently together. Clustering methods have been successfully used for this task due to the powerful property of creating spatial representations of the features and the abstractions detected from the input space. Self-organising map (SOM) is one of the most popular clustering techniques where abstractions are formed by mapping high dimensional input patterns into a lower dimensional set of output clusters. Most of the current uses of SOM for this task concentrated on clustering categorical features. fn this paper we present a new learning mechanism for self-organizing map which is useful when the aim is to extract patterns from a data set characterized by continuous input features. Furthermore the method used for network pruning and rule optimization is described. |
| first_indexed | 2025-11-14T07:08:49Z |
| format | Conference Paper |
| id | curtin-20.500.11937-14636 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:08:49Z |
| publishDate | 2005 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-146362022-10-20T04:39:12Z CSOM: self-organizing map for continuous data Hadzic, Fedja Dillon, Tharam S. Tharam Dillon Xinghuo Yu Elizabeth Chang Nowadays, lots of data is being collected for different industrial and commercial purposes, where the aim is to discover useful patterns from data which leads to discovery of valuable domain knowledge. Unsupervised learning is a useful method for these tasks as it requires no target class and it clusters the feature values that occur frequently together. Clustering methods have been successfully used for this task due to the powerful property of creating spatial representations of the features and the abstractions detected from the input space. Self-organising map (SOM) is one of the most popular clustering techniques where abstractions are formed by mapping high dimensional input patterns into a lower dimensional set of output clusters. Most of the current uses of SOM for this task concentrated on clustering categorical features. fn this paper we present a new learning mechanism for self-organizing map which is useful when the aim is to extract patterns from a data set characterized by continuous input features. Furthermore the method used for network pruning and rule optimization is described. 2005 Conference Paper http://hdl.handle.net/20.500.11937/14636 10.1109/INDIN.2005.1560466 IEEE restricted |
| spellingShingle | Hadzic, Fedja Dillon, Tharam S. CSOM: self-organizing map for continuous data |
| title | CSOM: self-organizing map for continuous data |
| title_full | CSOM: self-organizing map for continuous data |
| title_fullStr | CSOM: self-organizing map for continuous data |
| title_full_unstemmed | CSOM: self-organizing map for continuous data |
| title_short | CSOM: self-organizing map for continuous data |
| title_sort | csom: self-organizing map for continuous data |
| url | http://hdl.handle.net/20.500.11937/14636 |