政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/32618

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/32618

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | 全文笔数/总笔数 : 113822/144841 (79%)
造访人次 : 51750808 在线人数 : 299

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

搜寻范围

查询小技巧：

您可在西文检索词汇前后加上"双引号"，以获取较精准的检索结果

若欲以作者姓名搜寻，建议至进阶搜寻限定作者字段，可获得较完整数据

进阶搜寻

主页 ‧ 登入 ‧ 上传 ‧ 说明 ‧ 关于政大典藏 ‧ 管理

到手机版

政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 > Item 140.119/32618

请使用永久网址来引用或连结此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/32618

题名:	探勘空間相關樣式之研究 Mining Frequent Spatial Co-relation Patterns
作者:	黃郁君 Huang,Yu-Chun
贡献者:	沈錳坤 Huang,Man-Kwan 黃郁君 Huang,Yu-Chun
关键词:	資料探勘空間相關樣式 data mining spatial co-relation pattern
日期:	2003
上传时间:	2009-09-17 13:52:11 (UTC+8)
摘要:	在這個資訊快速擴張的時代，許多種類的資料庫被應用在各式各樣的領域中。空間資料探勘即是一個例子，它在空間資料庫中探勘出頻繁的樣式以及空間關係。空間資料探勘是在空間資料庫中挖掘出有趣的、以前不知道的、但實際上是有用的樣式或空間關係。在本篇論文中，我們探勘空間序列的問題。我們主要討論兩個主題：空間相關樣式，以及空間相似相關樣式。關於空間相關樣式，我們提出以Apriori為基礎以及深度優先為基礎的解法。在空間相關相似樣式部分，我們提出兩個演算法AP-mine以及AS-mine來解決我們的問題。在AP-mine中，我們提出一個名為AP-tree的資料結構來有效率的挖掘出空間相關相似樣式。最後我們以實驗來驗證我們的演算法。 With the growth of data, a variety of databases are applied in many applications. Spatial data mining is an example, and it discovers patterns or spatial relations from large spatial databases. Spatial data mining is the process of discovering interesting and previously unknown, but potential useful patterns or spatial relations from large spatial databases. In this thesis, we explore the problem of spatial sequential pattern mining. The two issues spatial co-relation patterns and approximate spatial co-relation patterns will be discussed. We utilize Apriori-based method and depth-first based method to solve the problem of spatial co-relation patterns. About approximate co-relation spatial patterns, we propose two algorithms, named AP-mine and AS-mine. In AP-mine, we propose a data structure, named AP-tree, to efficient mining the approximate spatial co-relation patterns. Lastly, We also perform the experiments to evaluate our spatial co-relation pattern mining algorithms.
參考文獻:	[1] R. Agarwal, C. C. Aggarwal, and V. V. V. Prasad. Depth First Generation of Long Patterns. Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2000. [2] R. Agarwal, C. C. Aggarwal, and V. V. V. Prasad. A Tree Projection Algorithm for Finding Frequent Itemsets. Journal on Parallel Distributed Computing, Vol. 61, No. 3, 2001. [3] R. Agrawal, T. Imielinski, and A. Swami. Mining Association Rules between Sets of Items in Large Databases. Proceedings of the ACM SIGMOD International Conference on Management of Data, 1993. [4] R. Agrawal and R. Srikant. Fast Algorithms for Mining Association Rules in Large Databases. Proceedings of the 20th International Conference on Very Large Data Bases, 1994. [5] S. K. Chang, Q. Y. Shi, and C. W. Yan, Iconic Indexing by 2D Strings, IEEE Transactions Pattern Analysis and Machine Intelligence, Vol. 9, No. 3, 1987. [6] S. Chawla, S. Shekhar, W. Wu, and U. Ozesmi. Modeling Spatial Dependencies for Mining Geospatial Data: An Introduction. In Geographic data mining and Knowledge Discovery (GKD), Harvey Miller and Jiawei Han (editors), 1999. [7] S. Chawla, S. Shekhar, W. Wu, and U. Ozesmi. Extending Data Mining for Spatial Applications: A Case Study in Predicting Nest Locations. ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, 2000. [8] M. S. Chen, J. Han, and P. S. Yu. Data Mining: An Overview from a Database Perspective, IEEE Transactions on Knowledge and Data Engineering, Vol. 8, No. 6, 1996. [9] J. Han, G. Dong, and Y. Yin. Efficient Mining of Partial Periodic Patterns in Time Series Database. Proceedings of the IEEE International Conference on Data Engineering, 1999. [10] J. Han, W. Gong, and Y. Yin. Mining Segment-Wise Periodic Patterns in Time-Related Databases. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1998. [11] J. Han, K. Koperski, and N. Stefanovic. GeoMiner: A System Prototype for Spatial Data Mining. Proceedings of the ACM SIGMOD International Conference on Management of Data, 1997. [12] J. Han, J. Pei, and Y. Yin. Mining Frequent Patterns without Candidate Generation. Proceedings of the ACM SIGMOD International Conference on Management of Data, 2000. [13] D. Hawkins. Identification of Outliers. Chapman and Hall, 1980. [14] Y. Huang, S. Shekhar, and H. Xiong. Discovering Co-location Patterns from Spatial Datasets: A General Approach. Submitted to IEEE Transactions on Knowledge and Data Engineering, under second round review, 2002. [15] Y. Huang, H. Xiong, S. Shekhar, and J. Pei. Mining Confident Co-location Rules without A Support Threshold. Proceedings of the 18th ACM Symposium on Applied Computing, 2003. [16] K. Koperski and J. Han. Discovery of Spatial Association Rules in Geographic Information Databases. Proceedings of the 4th International Symposium on Large Spatial Databases, 1995. [17] S. Y. Lee, M. K. Shan, and W. P. Yang. Similarity Retrieval of Iconic Image Databases. Pattern Recognition, Vol. 22, No. 6, 1989. [18] H. J. Loether and D. G. McTavish. Descriptive and Inferential Statistics: An Introduction. Allyn and Bacon, 1993. [19] Y. Morimoto. Mining Frequent Neighboring Class Sets in Spatial Databases. Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2001. [20] J. S. Park, M. S. Chen, and P. S. Yu. An Effective Hash-Based Algorithm for Mining Association Rules, Proceedings of ACM SIGMOD International Conference on Management of Data, 1995. [21] J. Pei, J. Han, B. Mortazavi-Asl, and H. Zhu. Mining Access Patterns Efficiently from Web Logs. Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2000. [22] G. Piatetsky-Shapiro and W. J. Frawley. Knowledge Discovery in Databases. AAAI∕MIT Press, 1991. [23] S. Shekhar and S. Chawla. Introduction to Spatial Data Mining. In Spatial Databases: A Tour, Prentice Hall, 2003. [24] S. Shekhar, S. Chawla, S. Ravada, A. Fetterer, X. Liu, and C. T. Lu. Spatial Databases: Accomplishments and Research Needs. IEEE Transactions on Knowledge and Data Engineering, Vol. 11, No. 1, 1999. [25] S. Shekhar and Y. Huang. Discovering Spatial Co-location Patterns: A Summary of Results. Proceedings of 7th International Symposium on Spatial and Temporal Databases, 2001. [26] S. Shekhar, Y. Huang, W. Wu, C. T. Lu, and S. Chawla, What`s Spatial about Spatial Data Mining: Three Case Studies. In Data Mining for Scientific and Engineering Applications, Kluwer Academic Publishers, 2001. [27] S. Shekhar, C. T. Lu, and P. Zhang. Detecting Graph-Based Spatial Outliers: Algorithms and Applications (Summary of Results). Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2001. [28] S. Shekhar, C. T. Lu, and P. Zhang. Detecting Graph-based Spatial Outliers. Intelligent Data Analysis, 2002. [29] S. Shekhar, C. T. Lu, and P. Zhang. A Unified Approach to Detecting Spatial Outliers. Geoinformatica, Vol. 7, Issue 2, 2003. [30] S. Shekhar, P. Schrater, R. Vatsavai, W. Wu, and S. Chawla. Spatial Contextual Classification and Prediction Models for Mining Geospatial Data. IEEE Transactions on Multimedia (special issue on Multimedia Databases), 2002. [31] S. Shekhar, P. Zhang, Y. Huang, and R. R. Vatsavai. Trends in Spatial Data Mining. In Data Mining: Next Generation Challenges and Future Directions, Hillol Kargupta and Anupam Joshi (editors), AAAI/MIT Press, 2003. [32] S. Shekhar, P. Zhang, Y. Huang, and R. R. Vatsavai. Spatial Data Mining, Proceedings of SIAM International Conference on Data Mining, 2003. [33] W. R. Tobler. Cellular Geography. In Philosophy in Geography, Dordrecht, 1979. [34] M. F. Worboys. GIS: A Computing Perspective. Taylor and Francis, 1995. [35] X. Yan, J. Han, and R. Afshar. CloSpan: Mining Closed Sequential Patterns in Large Datasets. Proceedings of SIAM International Conference on Data Mining, 2003.
描述:	碩士國立政治大學資訊科學學系 89753011 92
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0089753011
数据类型:	thesis
显示于类别:	[資訊科學系] 學位論文

文件中的档案:

档案	描述	大小	格式	浏览次数
75301101.pdf		42Kb	Adobe PDF2	873	检视/开启
75301102.pdf		59Kb	Adobe PDF2	875	检视/开启
75301103.pdf		68Kb	Adobe PDF2	934	检视/开启
75301104.pdf		39Kb	Adobe PDF2	819	检视/开启
75301105.pdf		29Kb	Adobe PDF2	1175	检视/开启
75301106.pdf		174Kb	Adobe PDF2	905	检视/开启
75301107.pdf		217Kb	Adobe PDF2	893	检视/开启
75301108.pdf		124Kb	Adobe PDF2	938	检视/开启
75301109.pdf		62Kb	Adobe PDF2	788	检视/开启
75301110.pdf		12Kb	Adobe PDF2	761	检视/开启
75301111.pdf		54Kb	Adobe PDF2	974	检视/开启

在政大典藏中所有的数据项都受到原著作权保护.

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

DSpace Software Copyright © 2002-2004 MIT & Hewlett-Packard / Enhanced by NTU Library IR team Copyright © - 回馈