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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳銘憲(Ming-Syan Chen) | |
| dc.contributor.author | Shih-Hsiang Lo | en |
| dc.contributor.author | 羅仕翔 | zh_TW |
| dc.date.accessioned | 2021-06-13T00:44:30Z | - |
| dc.date.available | 2007-07-31 | |
| dc.date.copyright | 2007-07-31 | |
| dc.date.issued | 2007 | |
| dc.date.submitted | 2007-07-25 | |
| dc.identifier.citation | [1] L. Breiman, J. H. Friedman, R. Olshen, and C. Sotne. Classification and Regression Trees.
Wadsworth, Belmont, 1984. [2] E. Brown, T. Brailsford, T. Fisher, A. Moore, and H. Ashman. Reappraising Cognitive Styles in Adaptive Web Applications. Proc. of the 15th International World Wide Web Conference, 2006. [3] N. A. R. Center. Introduction to IND Version 2.1. GA23-2475-02 edition, 1992. [4] P. Chesseman, J. Kelly, M. Self, and et al. AutoClass: A Bayesian classification system. In 5th Int’l Conf. on Machine Learning. Morgan Kaufman, 1988. [5] P. A. Chou. Optimal Partitioning for Classification and Resgression Trees. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 13, No 4, 1991. [6] EXIF. http://www.exif.org/. [7] U. Fayyad. On the Induction of Decision Trees for multiple Concept Learning. PhD thesis, The University of Michigan, Ann arbor, 1991. [8] U. Fayyad and K. B. Irani. Multi-interval discretization of continuous-valued attributes for classification learning. In Proc. of the 13th International Joint Conference on Artificial Intelligence, 1993. [9] M. Feng, N. T. Heffernan, and K. R. Koedinger. Addressing the Testing Challenge with a Web- Based E-Assessment System that Tutors as it Assess. Proc. of the 15th International World Wide Web Conference, 2006. [10] Y. Flickr. http://www.flickr.com/. [11] D. E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Morgan Kaufmann, 1989. [12] T. H. Haveliwala. Topic-Sensitive PageRank. Proc. of the 11th International World Wide Web Conference, 2002. [13] G. Jeh and J. Widom. Scaling Personalized Web Search. Proc. of the 12th International World Wide Web Conference, 2003. [14] J. Kelinberg. Authoritative sources in a hyperlinked environment. Proc. of ACM-SIAM Symposium on Discrete Algorithms, 1998. [15] C.-C. Lin and M.-S. Chen. VIPAS: Virtual Link Powered Authority Search in the Web. Proceedings of the 29th VLDB Conference, Berlin, Germany, 2003. [16] M. manager on Google map. http://www.google.com/apis/maps/documentation/. [17] M.Mehta, R. Agrawal, and J. Rissanen. SLIQ: A fast scalable classifier for data mining. In EDBT 96, Avignonm, France, 1996. [18] M. Mehta, J. Rissanen, and R. Agrawal. MDL-based decision tree pruning. In Int’l Conference on Knowledge Discovery in Databases and Data Mining, 1995. [19] D. Michie, D. Spiegelhalter, and C. Taylor. Machine Learning, Neural and Statistical Classification. Ellis Horwood, 1994. [20] L. Page. PageRank: Bringing order to the Web. Stanford Digital Libraries Working Paper, 1997. [21] G. Picasa. http://picasa.google.com/. [22] O. D. Project. http://domz.org/. [23] J. Quinlan. Induction of decision trees. Machine Learning, 1986. [24] J. Quinlan and R. L. Rivest. Inferring decision trees using minimum description length principle. Information and Computtation, 1989. [25] J. R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, 1993. [26] F. Radlinski and T. Joachims. Query Chains: Learning to Rank from Implicit Feedback. Proceedings of the Eleventh ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’05), 2005. [27] R. Rastogi and K. Shim. PUBLIC: A Decision Tree Classifier that Integrates Building and Pruning. Proceedings of 24rd International Conference on Very Large Data Bases, August 24-27, 1998, New York City, New York, USA, 1998. [28] B. D. Ripley. Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge, 1996. [29] J. Shafer, R. Agrawal, and M. Mehta. SPRINT: A scalable parallel classifier for data mining. In Proc. of the VLDB Conference, Bombay, India, 1996. [30] Zooomr. http://beta.zooomr.com/. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/29172 | - |
| dc.description.abstract | A pervasive web application is a server providingmany web services for its registered users. Nowadays,
three of basic services that a typical pervasive web application offers are membership management, search service and map-enabled photo service. In this thesis, we design a data mining framework composed of three different data mining techniques to improve the performance of three services. In order to improve the performance of membership management, in the second chapter, we develop a categorical decision tree classifier to classify users efficiently. It noted that the data of user profiles has an unique phenomenon. Its characteristic is that few attributes of user profiles have higher information gains to distinguish users. By exploiting this characteristic that a traditional decision tree classifier does not consider, our designed classifier can reduce the execution time in generating a decision tree for user classification. As a result, the decision tree generated by our classifier can identify users efficiently for special marketing needs of an advertisement. For the improvement of a search service, in the third chapter, we propose a sequential web search algorithm that leverages the sequential queries issued by users to search the required information. Compared with previous works, our approach uses the additional feedback data on result pages of sequential queries where prior works only use feedback data of a query. Thus, our approach can provide a better ranking of result pages for sequential queries. For the efficiency of retrieving geotagged photos, in the fourth chapter, we design a clustering algorithm that incrementally clusters geotagged photos in accordance to thresholds of different scales. Compared with other applications, we show the photo clusters instead of all photos where the number of photo clusters is much less than that of all photos. As a result, the performance of map-enabled photo service is improved efficiently. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-13T00:44:30Z (GMT). No. of bitstreams: 1 ntu-96-F86921019-1.pdf: 3068970 bytes, checksum: 74914b7449376f7f11e55b26418319bf (MD5) Previous issue date: 2007 | en |
| dc.description.tableofcontents | 1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . 1 1.2 Overview of the Dissertation . . . . 3 1.2.1 Inference Based Classifier: Efficient Construction of Decision Trees for Sparse Categorical Attributes . . . . . . . . . 3 1.2.2 Effective Sequential Web Search with Personal Page Eigenvectors . . . . . . . 4 1.2.3 Geotagged Photos Clustering Algorithm for of aMap-Enabled PhotoWeb Service 5 1.3 Organization of the Dissertation . . . . . 5 2 Inference Based Classifier: Efficient Construction of Decision Trees for Sparse Categorical Attributes 6 2.1 Introduction . . . .. . . . . . . . . . . . 6 2.2 Preliminaries . . . . . . . . . . . . . . . 9 2.3 Inference Based Classifier . . . . . . . . 10 2.3.1 Algorithm of IBC . . . . . . . . . . . . 10 2.4 PerformanceStudies . . . . . .. . . . . . 14 2.4.1 Real-life Datasets . . . . .. . . . 15 2.4.2 Experiment One: Classification Accuracy . . . . . . . 15 2.4.3 Experiment Two: Execution Time in Scale-Up Experiments for data set of sparse categorical attributes . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3 Effective SequentialWeb Search with Personal Page Eigenvectors 19 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.2 ProblemStatement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.3 Incremental Personal HITS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.3.1 ReviewofHITS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.3.2 IPHITSAlgorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.4 SystemFramework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.4.1 Design of a Search Proxy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.4.2 Design of Feedback Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.4.3 Design of Ranking Refinement . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.5 Experimental Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.5.1 System Architecture of TOP Platform . . . . . . . . . . . . . . . . . . . . . . 32 3.6 Experimental Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 4 Geotagged Photos Clustering Algorithm for of a Map-Enabled PhotoWeb Service 40 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 4.1.1 ProposedFrameworkof aMap-EnabledPhotoWebService . . . . . . . . . . 46 4.2 RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 4.3 ProblemStatement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 4.4 Geotagged Photo Clustering Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . 49 4.4.1 UsageScenario1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 4.4.2 UsageScenario2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 4.4.3 Design of the Client Program . . . . . . . . . . . . . . . . . . . . . . . . . . 52 4.4.4 Design of the Server Program . . . . . . . . . . . . . . . . . . . . . . . . . . 52 4.4.5 Design of an Incremental Framework . . . . . . . . . . . . . . . . . . . . . . 52 4.4.6 photo Clustering Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 4.4.7 Design of User Clustering Algorithm . . . . . . . . . . . . . . . . . . . . . . 52 4.4.8 Design of a Map Service . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 4.5 Experimential System Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 4.5.1 The Architecture of a Client-SideProgram . . . . . . . . . . . . . . . . . . . 54 4.5.2 The Architecture of a Server-SideProgram . . . . . . . . . . . . . . . . . . . 54 4.6 Scenario of Data Synchronous Mechanism . . . . . . . . . . . . . . . . . . . . . . . . 54 4.6.1 On-Line Synchronous Mode . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 4.6.2 Off-Line Asynchronous Mode . . . . . . . . . . . . . . . . . . . . . . . . . . 58 4.7 Experimental Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 4.7.1 The user-interface of the client-program . . . . . . . . . . . . . . . . . . . . . 58 4.7.2 The user-interface of the server-program. . . . . . . . . . . . . . . . . . . . . 64 4.7.3 The Experimental Result of DisplayingDifferentphotoClusters . . . . . . . . 64 4.7.4 The Experimental Result of DisplayingDifferentMapScales . . . . . . . . . . 66 4.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 5 Conclusions 75 | |
| dc.language.iso | en | |
| dc.subject | 經緯度叢集法 | zh_TW |
| dc.subject | 個人化搜尋 | zh_TW |
| dc.subject | 決策樹 | zh_TW |
| dc.subject | 資訊勘測 | zh_TW |
| dc.subject | pervasive applications | en |
| dc.subject | geotagged clustering | en |
| dc.subject | personalized search | en |
| dc.subject | decision tree | en |
| dc.subject | data mining | en |
| dc.title | 應用於廣泛網路應用之資訊勘測 | zh_TW |
| dc.title | Mining Framework for Pervasive Applications | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 95-2 | |
| dc.description.degree | 博士 | |
| dc.contributor.oralexamcommittee | 廖婉君,陳孟彰,李瑞庭,呂永和,楊德年 | |
| dc.subject.keyword | 資訊勘測,決策樹,個人化搜尋,經緯度叢集法, | zh_TW |
| dc.subject.keyword | pervasive applications,data mining,decision tree,personalized search,geotagged clustering, | en |
| dc.relation.page | 79 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2007-07-25 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
| 顯示於系所單位： | 電機工程學系 | |
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|---|---|---|---|
| ntu-96-1.pdf 未授權公開取用 | 3 MB | Adobe PDF |
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