Document Clustering using Self-Organizing Maps
| dc.contributor.author | Rafi, Muhammad | |
| dc.contributor.author | Waqar, Muhammad | |
| dc.contributor.author | Ajaz, Hareem | |
| dc.contributor.author | Ayub, Umar | |
| dc.contributor.author | Danish, Muhammad | |
| dc.coverage.issue | 1 | cs |
| dc.coverage.volume | 23 | cs |
| dc.date.accessioned | 2019-06-26T10:18:09Z | |
| dc.date.available | 2019-06-26T10:18:09Z | |
| dc.date.issued | 2017-06-01 | cs |
| dc.description.abstract | Cluster analysis of textual documents is a common technique for better ltering, navigation, under-standing and comprehension of the large document collection. Document clustering is an autonomous methodthat separate out large heterogeneous document collection into smaller more homogeneous sub-collections calledclusters. Self-organizing maps (SOM) is a type of arti cial neural network (ANN) that can be used to performautonomous self-organization of high dimension feature space into low-dimensional projections called maps. Itis considered a good method to perform clustering as both requires unsupervised processing. In this paper, weproposed a SOM using multi-layer, multi-feature to cluster documents. The paper implements a SOM usingfour layers containing lexical terms, phrases and sequences in bottom layers respectively and combining all atthe top layers. The documents are processed to extract these features to feed the SOM. The internal weightsand interconnections between these layers features(neurons) automatically settle through iterations with a smalllearning rate to discover the actual clusters. We have performed extensive set of experiments on standard textmining datasets like: NEWS20, Reuters and WebKB with evaluation measures F-Measure and Purity. Theevaluation gives encouraging results and outperforms some of the existing approaches. We conclude that SOMwith multi-features (lexical terms, phrases and sequences) and multi-layers can be very e ective in producinghigh quality clusters on large document collections. | en |
| dc.format | text | cs |
| dc.format.extent | 111-118 | cs |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Mendel. 2017 vol. 23, č. 1, s. 111-118. ISSN 1803-3814 | cs |
| dc.identifier.doi | 10.13164/mendel.2017.1.111 | en |
| dc.identifier.issn | 2571-3701 | |
| dc.identifier.issn | 1803-3814 | |
| dc.identifier.uri | http://hdl.handle.net/11012/179206 | |
| dc.language.iso | en | cs |
| dc.publisher | Institute of Automation and Computer Science, Brno University of Technology | cs |
| dc.relation.ispartof | Mendel | cs |
| dc.relation.uri | https://mendel-journal.org/index.php/mendel/article/view/61 | cs |
| dc.rights.access | openAccess | en |
| dc.subject | Document Clustering | en |
| dc.subject | Text Mining | en |
| dc.subject | Neural Network | en |
| dc.subject | Unsupervised Learning | en |
| dc.subject | Self-Organizing Maps | en |
| dc.subject | Layered Approach | en |
| dc.title | Document Clustering using Self-Organizing Maps | en |
| dc.type.driver | article | en |
| dc.type.status | Peer-reviewed | en |
| dc.type.version | publishedVersion | en |
| eprints.affiliatedInstitution.faculty | Fakulta strojního inženýrství | cs |
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