مؤتمر
A novel clustering algorithm based on searched experiences
| العنوان: | A novel clustering algorithm based on searched experiences |
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| المؤلفون: | Tsai, C.-W., Ding, Y.-C., Chiang, M.-C., Yang, C.-S. |
| المساهمون: | Institute of Computer and Communication Engineering |
| بيانات النشر: | Institute of Electrical and Electronics Engineers Inc. |
| سنة النشر: | 2017 |
| المجموعة: | National Cheng Kung University: NCKU Institutional Repository / 國立成功大學機構典藏 |
| مصطلحات موضوعية: | And k-means, Clustering, Data mining |
| الوصف: | SCOPUS ; How to reduce the computation time and how to improve the quality of the clustering result are the two major research issues. Although several efficient and effective clustering algorithms have been presented, none of which is perfect. As such, an effective clustering algorithm, which is based on the prediction of searching information to determine the search directions at later iterations and employs the k-means as the local search operator to fine-tune the end result, is presented in this paper. Simulation results show that the proposed algorithm is less sensitive to the initial random solution; thus, it is capable of providing a better result than the other clustering algorithms compared in this paper in terms of the quality of the clustering result. ? 2017 IEEE. |
| نوع الوثيقة: | conference object |
| وصف الملف: | 106 bytes; text/html |
| اللغة: | English |
| العلاقة: | 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017, Vol. 2017-Janua, pp. 804-808; 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017, 5 October 2017 through 8 October 2017; http://140.116.207.99/handle/987654321/218707Test; http://140.116.207.99/bitstream/987654321/218707/1/index.htmlTest |
| DOI: | 10.1109/SMC.2017.8122707 |
| الإتاحة: | https://doi.org/10.1109/SMC.2017.8122707Test http://140.116.207.99/handle/987654321/218707Test http://140.116.207.99/bitstream/987654321/218707/1/index.htmlTest |
| رقم الانضمام: | edsbas.9F140D4B |
| قاعدة البيانات: | BASE |
| DOI: | 10.1109/SMC.2017.8122707 |
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