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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳建錦(Chien-Chin Chen) | |
| dc.contributor.author | Chiu-Chong Chen | en |
| dc.contributor.author | 陳秋中 | zh_TW |
| dc.date.accessioned | 2021-06-17T01:33:27Z | - |
| dc.date.available | 2019-08-07 | |
| dc.date.copyright | 2017-08-07 | |
| dc.date.issued | 2017 | |
| dc.date.submitted | 2017-08-02 | |
| dc.identifier.citation | 1. Johnson, S. C. (1967). Hierarchical clustering schemes. Psychometrika, 32(3), 241-254.
2. Nascimento, M. A., Sander, J., & Pound, J. (2003). Analysis of SIGMOD's co-authorship graph. ACM Sigmod record, 32(3), 8-10. 3. Allan, J., Aslam, J., Belkin, N., Buckley, C., Callan, J., Croft, B., ... & Hiemstra, D. (2003, April). Challenges in information retrieval and language modeling: report of a workshop held at the center for intelligent information retrieval, University of Massachusetts Amherst, September 2002. In ACM SIGIR Forum(Vol. 37, No. 1, pp. 31-47). ACM. 4. Liu, X., Bollen, J., Nelson, M. L., & Van de Sompel, H. (2005). Co-authorship networks in the digital library research community. Information processing & management, 41(6), 1462-1480. 5. Tight, M. (2008). Higher education research as tribe, territory and/or community: A co-citation analysis. Higher Education, 55(5), 593-605. 6. Ding, Y. (2011). Scientific collaboration and endorsement: Network analysis of coauthorship and citation networks. Journal of informetrics, 5(1), 187-203. 7. Newman, M. E. (2004). Detecting community structure in networks. The European Physical Journal B-Condensed Matter and Complex Systems, 38(2), 321-330. 8. Smyth, P. (1996, August). Clustering Using Monte Carlo Cross-Validation. In Kdd (Vol. 1, pp. 26-133). 9. Roth, V., Lange, T., Braun, M., & Buhmann, J. (2002, July). A resampling approach to cluster validation. In International conference on computational statistics (Vol. 15, pp. 123-128). 10. Dunn, J. C. (1974). Well-separated clusters and optimal fuzzy partitions. Journal of cybernetics, 4(1), 95-104. 11. Davies, D. L., & Bouldin, D. W. (1979). A cluster separation measure. IEEE transactions on pattern analysis and machine intelligence, (2), 224-227. 12. Tibshirani, R., Walther, G., & Hastie, T. (2001). Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63(2), 411-423. 13. Salvador, S., & Chan, P. (2005). Learning states and rules for detecting anomalies in time series. Applied Intelligence, 23(3), 241-255. 14. Newman, M. E. (2006). Modularity and community structure in networks. Proceedings of the national academy of sciences, 103(23), 8577-8582. 15. Blondel, V. D., Guillaume, J. L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of statistical mechanics: theory and experiment, 2008(10), P10008. 16. Waltman, L., & van Eck, N. J. (2013). A smart local moving algorithm for large-scale modularity-based community detection. The European Physical Journal B, 86(11), 471. 17. Church, K. W., & Hanks, P. (1990). Word association norms, mutual information, and lexicography. Computational linguistics, 16(1), 22-29. 18. Frénay, B., Doquire, G., & Verleysen, M. (2014). Estimating mutual information for feature selection in the presence of label noise. Computational Statistics & Data Analysis, 71, 832-848. 19. Zhao, Y., & Karypis, G. (2001). Criterion functions for document clustering: Experiments and analysis (Vol. 1, p. 40). Technical report. 20. Fortunato, S., & Barthélemy, M. (2007). Resolution limit in community detection. Proceedings of the National Academy of Sciences, 104(1), 36-41. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/67466 | - |
| dc.description.abstract | 本論文的主旨在於研究並利用智慧局部移動法(Smart Local Moving) 於生醫領域相關研究資源的群集尋找上，我們根據實際的平台資料先定義了一個網路模型，並利用點與點之間估計互信息(Estimating Mutual Information) 作為網路中節點間的邊之權重值。以這個模型我們建立了一個查找特定資源與最相關資源的服務。
此外根據這樣的模型我們利用SLM進行分群，並根據Dunn-Index與資料特性自定義了Coherence Index作為群集大小與數量的判別依據，最後以10-folds Cross-Validation進行實驗得出最佳的分群結果，供未來與相關領域專業人員，並進一步改良之討論依據。 | zh_TW |
| dc.description.abstract | A peculiar thing about biomedical researches is that they generally involve more than related past works and literature. Tools, softwares, databases and even samples are considered. Even though many online services have been developed to collect and share these resources. Still, they are too many for researchers to find desire information. For instance, SciCrunch, as one of largest online resource platform, contains more than 15,000 resources.
In this research, we present the analytic result for biomedical research resources in order to figure out meaningful groups of biomedical resources with community detection. We apply the Smart Local Moving algorithm to detect meaningful communities inside the network of resources. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T01:33:27Z (GMT). No. of bitstreams: 1 ntu-106-R04725031-1.pdf: 2287622 bytes, checksum: e9fa0580973f5a672233a95a435170a4 (MD5) Previous issue date: 2017 | en |
| dc.description.tableofcontents | Chapter 1. Introduction 1
Chapter 2. Related Works 3 2.1 Methods Applied for Scholar Research 3 2.2 Determining Number of Communities 5 2.3 Modularity-based Clustering 6 Chapter 3. Methodology 7 3.1 Problem Definition 7 3.2 Estimated Mutual Information for Edges Weights 7 3.3 Clustering Algorithm 10 Chapter 4. Experiment 13 4.1 Dataset 13 4.2 Metrics 14 4.2.1 Weighted Cross-Cluster Accuracy 15 4.2.2 Inverse-Purity 17 4.2.3 Coherence Index 18 4.3 Result 20 Chapter 5. Conclusion 25 Chapter 6. Reference 27 | |
| dc.language.iso | en | |
| dc.subject | 網路分析 | zh_TW |
| dc.subject | 學術網路 | zh_TW |
| dc.subject | 群集辨識 | zh_TW |
| dc.subject | 分群演算法 | zh_TW |
| dc.subject | 群集驗證 | zh_TW |
| dc.subject | Clustering | en |
| dc.subject | Scholar Network | en |
| dc.subject | Communities Detection | en |
| dc.subject | Network Analysis | en |
| dc.subject | Cluster Validation | en |
| dc.title | 生醫研究資源群集判別 | zh_TW |
| dc.title | Community detection for Biomedical Research Resources | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 105-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 張詠淳(Yung-Chun Chang),陳孟璋(Meng-Chang Chen) | |
| dc.subject.keyword | 網路分析,學術網路,群集辨識,分群演算法,群集驗證, | zh_TW |
| dc.subject.keyword | Network Analysis,Scholar Network,Communities Detection,Clustering,Cluster Validation, | en |
| dc.relation.page | 29 | |
| dc.identifier.doi | 10.6342/NTU201702368 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2017-08-02 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
| 顯示於系所單位： | 資訊管理學系 | |
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| ntu-106-1.pdf 未授權公開取用 | 2.23 MB | Adobe PDF |
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