閱讀全文 | |
篇名 |
A Novel Rough Fuzzy Clustering Algorithm with A New Similarity Measurement
|
---|---|
並列篇名 | A Novel Rough Fuzzy Clustering Algorithm with A New Similarity Measurement |
作者 | Yang Li、Jian-cong Fan、Jeng-Shyang Pan、Gui-han Mao、Geng-kun Wu |
英文摘要 | With the emergence of exponential growth of datasets in various fields, fuzzy theory-based approaches are widely used to improve or optimize the data clustering algorithms. These improved algorithms can achieve better results than the original counterparts in practical applications. However, the fuzzy clustering algorithms including the traditional improved algorithms normally ignore the clustering boundary uncertainty, inter-class compactness and complex data problems, thereby result in the unsatisfactory clustering results. To address this issue, in this paper, a novel rough fuzzy clustering algorithm based on a new similarity measure is proposed by utilizing the upper approximation and lower approximation of rough set. We also develop the method of transforming fuzzy clustering model into rough set model. Our experiment results show that the improved algorithm can get better clustering effect. |
起訖頁 | 1145-1156 |
關鍵詞 | Similarity measure、Fuzzy clustering algorithm、Rough set、Clustering |
刊名 | 網際網路技術學刊 |
期數 | 201907 (20:4期) |
出版單位 | 台灣學術網路管理委員會 |
DOI |
|
QR Code | |
該期刊 上一篇
| Efficient Certificate Based One-pass Authentication Protocol for IMS |
該期刊 下一篇
| A Forwarder Based Temperature Aware Routing Protocol in Wireless Body Area Networks |