| dc.contributor | 資管系 | |
| dc.creator (作者) | 蔡瑞煌 | zh_TW |
| dc.creator (作者) | Tsaih, Rua-Huan;Lin, Wan-Ying;Huang, Shin-Ying | |
| dc.date (日期) | 2009 | |
| dc.date.accessioned | 12-Feb-2015 12:23:37 (UTC+8) | - |
| dc.date.available | 12-Feb-2015 12:23:37 (UTC+8) | - |
| dc.date.issued (上傳時間) | 12-Feb-2015 12:23:37 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/73492 | - |
| dc.description.abstract (摘要) | The issue of fraudulent financial reporting has drawn much public as well as academic attention. However, most relevant researches focus on predicting financial distress or bankruptcy. Little emphasis has been placed on exploring the financial reporting fraud itself. This study addresses the challenge of obtaining an enhanced understanding of the financial reporting fraud through the approach with the following four phases: (1) to identify a set of financial and corporate governance indicators that are significantly correlated with fraudulent financial reporting; (2) to use the Growing Hierarchical Self-Organizing Map (GHSOM) to cluster data from listed companies into fraud and non-fraud subsets; (3) to extract knowledge from the fraudulent financial reporting through observing the hierarchical relationship displayed in the trained GHSOM; and (4) to provide justification to the extracted knowledge. | |
| dc.format.extent | 865083 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.relation (關聯) | Intelligence and Security Informatics Lecture Notes in Computer Science Volume,5477,31-41 | |
| dc.subject (關鍵詞) | Financial Reporting Fraud; Growing Hierarchical Self-Organizing Map; Knowledge Extraction | |
| dc.title (題名) | Exploring Fraudulent Financial Reporting with GHSOM | |
| dc.type (資料類型) | article | en |
| dc.identifier.doi (DOI) | 10.1007/978-3-642-01393-5_5 | en_US |
| dc.doi.uri (DOI) | http://dx.doi.org/10.1007/978-3-642-01393-5_5 | en_US |
