| Başlık: | Use of Growing Self Organizing Maps in Text Mining |
| Yazar(lar): | Yazar #1 Ad Soyad: Zafer İşcan Kurum: İstanbul Teknik Üniversitesi Ülke: Turkey E-Posta: iscanz__at__itu.edu.tr |
| Anahtar Kelimeler: | Self-Organizing Map, Data Mining, Text Mining |
| Özet: | Use of Growing Self Organizing Maps in Text Mining In the study, growing self-organizing map (GSOM) based two papers that perform different approaches for decreasing clustering time in text mining are examined. In the first paper, a clustering method using growing self organizing map in two steps is presented. Most significant amount of the clustering process is divided into sub-processes which can be performed in different computers by using the evolving grid technology. Therefore, a quick analysis of the acquired information becomes possible. Performance of the proposed method is close to the traditional approaches. However, application time is improving 15 times. In the second paper, a new algorithm (HDGSOMr) based on a growing version of SOM is introduced. The new algorithm adds randomness to the self-organizing process in order to generate more qualified clusters in a few iterations by using smaller neighborhood. Thus, total time of the operation decreases significantly. |
| Konu(lar): | Veri Madenciliği |
| Dosya: | 106.doc |