摘要
文本挖掘是一个非常活跃的研究领域,是数据挖掘领域的一个重要分支。文本挖掘采用了很多传统的数据挖掘技术,但又有自己的特性。本文试图采用支持向量机,流形学习与图论等理论,以网络文本为研究对象,全面开展文本分类、聚类、压缩、可视化及排序等方面的算法研究。全文的主要工作包括以下几个方面:
1)在定理证明的基础上,提出一种连分式Mercer核,它可以方便地应用于支持向量分类机和其它支持向量机算法。在5个UCI数据库实验中取得了比传统核支持向量机更好的综合水平,而且它还可以方便地被用于合成复杂核,将此连分式核的支持向量机应用到网络文本分类中,提高了网络文本分类正确率。
2)提出了两个判别性的特征提取方法–判别性PCA和判别性KPCA。基于PCA和MMC理论,构造了一个多目标规划模型作为特征提取的目标。随后,该模型被转化成一个单目标规划问题并通过特征分解的方法求解。此外,将一个近似分块对角核矩阵K分成c个小矩阵并求出它们的特征值和特征向量,在此基础上,通过张量代数处理得到一种映射矩阵V,核矩阵投影到V上后能最大程度上保持同类样本间的相似信息,同时还能让类间距离变得更大。
3)提出了一种新的基于支持向量回归的偏好学习算法。它克服了偏好学习不一致问题并改善了排序的泛化能力。同时,WMW统计量被引入以评价算法的排序表现。在一个人工数据集和几个基准数据集上的实验显示了方法的有效性。最后,该方法还被应用到网络搜索系统的排序问题中,获得了较高的排序准确率。
4)共享最近邻(SNN)相似度是一种新的相似性度量,它能克服样本间相似性低和类密度差异大的问题。目前,基于SNN相似度的聚类算法有JP聚类和基于SNN密度聚类两种。它们的聚类结果完全依赖于单链的强度,因而算法非常脆弱。引入计算几何学中的光滑拼接思想,设计了一种新的基于SNN相似度的光滑拼接聚类算法。它内含强度-光滑度互补机制,相比已有的两种算法,该算法的泛化能力较高。在公开的文本数据集上做比较实验,结果显示,该算法在多个类别上取得了最高的聚类准确率和召回率。
5)针对互联网开放性、层次性、演化性、巨量性等本质特性,从复杂自适应系统这一全新的角度,以农业垂直搜索为应用背景,提出一种新的复杂自适应搜索模型。该搜索模型的主要特点是通过建立信息采集、分类、清洗与服务智能体联盟,组成多智能体实验环境;通过建立模型的学习机制与进化机制,改善搜索模型对网络环境的动态适应能力。经过与现有主流搜索引擎的比较实验发现,它在查准率方面具有明显的优势。同时,由于该搜索模型具备通用的结构体系,因而在建立其它行业的垂直搜索模型时它可以被方便地移植使用。
Text mining is a very active studying field, and is an important offset of data mining. It has made full use of the traditional techniques for data mining, and also needs some special methods matching the characters of text. We try to apply support vector machines, manifold learning and graphic theory to design some practical algorithms including classification, clustering, compression, visualization and ranking for text mining. The main works in this thesis can be introduced as follows:
1. Based on the proof of a series of Theorems, this paper presents a new continued fraction Mercer kernel, which can be used in SVC algorithm and other SVM algorithm. Experimental results show the SVC algorithm with continued fraction kernel works successfully on real data, and is competitive to the other existing simple kernels. Moreover, this kernel can be used to combine relatively complex kernels such as RBF applying kernel tricks easily.
2. In this paper, two novel methods for dimensionality reduction– modified PCA and modified Kernel PCA are proposed. Based on the theory of PCA and Maximizing Margin Criterion, we construct a multi-objective project model to formalize our goals for dimensionality reduction. Then it is transformed into a single-objective cost function for the projection and the optimal linear mapping is obtained through optimizing this cost function. Additionally, we divide the nearly diagonal block kernel matrix into c kernel matrixes and use eigendecomposition method to solve their d principal vectors based on which the d approximate eigenvectors of original kernel matrix K are obtained, then the combined mapping V can be used to reduce dimensionality in which inner-class information is preserved efficiently and it can cover more larger dataset than kernel PCA. Finally, the two methods are applied to compress some datasets and the results show their validity.
3. In this paper we propose a novel approach of learning preference relations using Support Vector Regression (SVR). It overcomes the problem of inconsistencies of preference and improves the ability of generalization to ranking for the property of SVR method. Meanwhile, the WMW statistic is introduced to evaluate the result of the ranking algorithm. The experiments on an artificial dataset and some benchmark datasets show the effectiveness of the proposed algorithm. An application to ranking in web searching system based on the proposed method is also demonstrated.
4. Sharing nearest neighbor (SNN) similarity is a newly metric measure of similarity, and it can conquer the two difficulties: the low similarity between samples and the different density of class. At present, there are two popular SNN similarity based clustering methods: JP clustering and SNN density based clustering. The clustering results of applying them highly rely on the weighting value of the single edge, thus they are very vulnerable. Motivated by the thinking of smooth merge in computing geometry, the authors design a novel SNN density based clustering algorithm. Since it inherits complementary intensity - smoothness principle, its generalizing ability surpasses those of the other two methods. The result of experiment on a public text dataset also shows our method access the best clustering precision and recall accuracy in most cases.
5. The Internet has the characteristics of openness, hierarchy, evolution, mass and is a typical complex adaptive system. So a new complex adaptive search model is proposed based on the theory of complex adaptive system. Through establishing the main union of information collection, classification, cleaning and services, a multi-agent experiment environment is formed. The learning mechanism and evolutionary mechanism are also be researched so that the search engine with the new model can be actively adapted to the complex and dynamic network environment. Meanwhile, this model can be widely used to construct those special search models.
引文
[1]. V. Vapnik, The Nature of Statistical Learning Theory, Springer, New York, 1995.
[2]. V. Vapnik, Statistical Learning Theory, Addison-Wiley, Reading, MA, 1998
[3]. N. Cristianini, J. Schawe-Taylor, An Introduction to Support Vector Machines, Cambridge University Press, Cambridge, 2000.
[4]. K.R. Müller, S. Mika, An introduction to kernel-based learning algorithms, IEEE Trans. Neural Networks 12 (2) 181–201, 2001.
[5]. Poggio T, On optimal nonlinear associative recall, Biological Cybernetics, 19:201-209, 1975.
[6]. Poggio T, Girosi F, Networks for approximation and learning, Proceedings of the IEEE, 78(9), September 1990.
[7]. Sch(0|¨)lkopf B, Support vector learning, R. Oldenbourg Verlag, 1997.
[8]. Amri S, Wu S, Improving support vector machine classifiers by modifying kernel functions, Neural Networks, 1999.
[9]. Watkins C, Dynamic alignment kernels, Advances in Large Margin Classifiers, MIT Press, 1999.
[10]. Haussler D, Convolution kernels on discrete structures, Technical Report UCSC-CRL-99-10, University of California in Santa Cruz, Computer Science Department, July 1999.
[11]. Watkins C, Kernels from matching operations, Technical Report CSD-TR-98-07, Royal Holloway, University of London, Computer Science Department, July 1999.
[12]. Ferreira de Oliveira MC, Levkowitz H. From visual data exploration to visual data mining: a survey. IEEE Trans Vis Computer Graph 2003; 9(3):207-216.
[13]. Spence R.Information visualization. New York, USA: ACMPress/Addison-Wesley, 2001.
[14]. Card SK, Mackinglay JD, Shneiderman B. Readings in information visualization–using vision to think. San Francisc, 1999.
[15]. Tufte ER. Envisioning Information. Cheshire, USA: Graphics Press, 1990.
[16]. Han J, Kamber M. Data mining: concepts and techniques. San Francisco, USA: Morgan Kaufmann, 2001.
[17]. Hu Shu-Li. Fuzzy Mathematics and Applications. Si Chuan University Press, Cheng Du, 1994.
[18]. Ta-Te Lin. Development of a virtual reality GIS using stereo vision. Computers and Electronics in Agriculture 63 (2008), p38–48.
[19]. Roweis S, Saul L. Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science, Vol 290, 2000, p2323-2326.
[20]. Tenenbaum J, Silva V D, Langford J. A global geometric framework for nonlinear dimensionality reduction. Science, Vol 290, 2000, p2319-2323.
[21]. Deerwester s, Dumais S, Furnas G, Landauer T, Harshman R. Indexing by latent semantic analysis. Journal of the American Society of Information Science, 1990.
[22]. Li H, Yamanishi K. Mining from open answers in questionnaire data. In: Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, California, pp443-449, 2001.
[23]. R.Herbrich, T.Graepel, P.Bollmann-Sdorra, and K.Obermayer. Learning Preference Relations for Information Retrieval. Int’l Conf. Machine Learning Workshop Learning for Text Categorization, pp. 80-84, 1998.
[24]. T.Joachims. Optimizing Search Engines Using Clickthrough Data. Eighth ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining,pp.133-142, 2002.
[25]. C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton and G. Hullender. Learning to Rank Using Gradient Descent. 22nd Int’l Conf. Machine Learning, 2005.
[26]. Y. Freund, R. Iyer, and R. Schapire. An Efficient Boosting Algorithm for Combining Preferences. J. Machine Learning Research, vol. 4, pp. 933-969, 2003.
[27]. O. Dekel, C. Manning, and Y. Singer. Log-Linear Models for Label Ranking. Advances in Neural Information Processing Systems 16, S. Thrun, L. Saul, and B. Sch?lkopf, eds. MIT Press, 2004.
[28]. W. Chu and Z. Ghahramani. Preference Learning with Gaussian Processes. 22nd Int’l Conf. Machine Learning, pp. 137-144, 2005.
[29]. G. Fung, R. Rosales, and B. Krishnapuram. Learning Rankings via Convex Hull Separation. Advances in Neural Information Processing Systems 18, MIT Press, 2006.
[30]. R. Yan and A. Hauptmann. Efficient Margin-Based Rank Learning Algorithms for Information Retrieval. Int’l Conf. Image and Video Retrieval, 2006.
[31]. C. Burges, R. Ragno, and Q. Le. Learning to Rank with Nonsmooth Cost Functions. Advances in Neural Information Processing Systems 19, B. Sch?lkopf, J. Platt, and T. Hoffman, eds.MIT Press, 2007.
[32]. E.F. Harrington. Online Ranking/Collaborative Filtering Using the Perceptron Algorithm. 20th Int’l Conf. Machine Learning, 2003.
[33]. Vikas C. Raykar, Ramani Duraiswami, and Balaji Krishnapuram. A Fast Algorithm for Learning a Ranking Function from Large-Scale Data Sets. IEEE transactions on pattern analysis and machine intelligence, vol. 30, no. 7, pp.1158-1170, 2008.
[34]. Hand, D.J., Mannila, H., Smyth, P. Principles of Data Mining (Adaptive Computation and Machine Learning), MIT Press, 2001.
[35]. Porter, M.F. Algorithm for suffix striping, Program, 130-137, 1980.
[36]. Sebastiani, F. Machine learning in automated text categorization, ACM Comput. Surv., Vol 34, 1, 1-47, 2002.
[37]. Berry, M., Browne, M. Understanding Search Engines: Mathematical Modeling and Text Retrieval (Software, Environments, Tools), SIAM, 2005.
[38]. Martinez, A framework for the representation of semantics, Ph.D. thesis, George Mason University, 2002.
[39]. Lodhi, H., Saunders, C., Shawe-Taylor, J., Cristianini, N., Watkins, C. Text classification using string kernels, J. Mach. Learn., 2, 419-444, 2002.
[40]. Bird, S., Klein, E., Loper, E. Natural Language Processing in Python, Creative Commons Attribution-NonCommericial–No Derivative Works 3.0, New York, 2007.
[41]. Case Y, a. Z. M. Development of an automated indexing system based on Chinese words segmentation and its application. Journal of information science 10: pp.352-367,1991.
[42]. Fan, C. K., Tsai, and W. H. Automatic word identification in Chinese sentences by the relaxation technique. Computer processing of Chinese and oriental Languages 4(1): pp.35-56,1998.
[43]. Sproat R, a. S. C. A statistical method for finding word boundaries in Chinese text, Computer processing of Chinese and oriental languages 4: pp.336-351,1990.
[44]. Nie J, B. M., and Ren X. On Chinese text retrieval. In: proceedings of the 19th annual international ACM SIGIR conference on research and development in information retrieval, 2002.
[45]. Manning, C. D. Schütze, H. Foundations of Statistical Natural Language Processing (Hardcover), MIT, 1999.
[46]. Duda, R. O., Hart, P. E., Stork, D. G. Pattern Classification, Seconded. Wiley Interscience. 2000.
[47]. K.Kalpakis, D. Gada, and V.Puttagunta. Distance Measures for Effective Clustering of ARIMA Time-Series. In Proc.of the 2001 IEEE Intl. Conf. on Data Mining, pp.273-280, 2001.
[48]. E. J. Keogh, M. J. Pazzani. Scaling up dynamic time warping for data mining applications. In KDD, pp.285-289, 2000.
[49]. M.R. Anderberg. Cluster Analysis for Applications. Academic Press, New York, December 1973.
[50]. A. K. Jain, R.C. dubes. Algorithms for Clustering Data. Prentice Hall Advanced Reference Series. Prentice Hall, March 1988.
[51]. L. Kaufman, P.J. Rousseeuw. Finding Groups in Data: An Introduction to Cluster Analysis. Wiley Series in Probability and Statistics. John Wiley and Sons, New York, November 1990.
[52]. P.H.A. Sneath, R.R. Sokal. Numerical Taxonomy. Freeman, San Francisco, 1971.
[53]. V. Vapnik, The Nature of Statistical Learning Theory, Springer, New York, 1995.
[54]. V. Vapnik, Statistical Learning Theory, Addison-Wiley, Reading, MA, 1998
[55]. N. Cristianini, J. Schawe-Taylor, An Introduction to Support Vector Machines, Cambridge University Press, Cambridge, 2000.
[56]. K.R. Müller, S. Mika, An introduction to kernel-based learning algorithms, IEEE Trans. Neural Networks 12 (2) 181–201, 2001.
[57]. Poggio T, On optimal nonlinear associative recall, Biological Cybernetics, 19:201-209, 1975.
[58]. Poggio T, Girosi F, Networks for approximation and learning, Proceedings of the IEEE, 78(9), September 1990.
[59]. Sch(O|¨)lkopf B, Support vector learning, R. Oldenbourg Verlag, 1997.
[60]. Amri S, Wu S, Improving support vector machine classifiers by modifying kernel functions, Neural Networks, 1999.
[61]. Watkins C, Dynamic alignment kernels, Advances in Large Margin Classifiers, MIT Press, 1999.
[62]. Haussler D, Convolution kernels on discrete structures, Technical Report UCSC-CRL-99-10, University of California in Santa Cruz, Computer Science Department, July 1999.
[63]. Watkins C, Kernels from matching operations, Technical Report CSD-TR-98-07, Royal Holloway, University of London, Computer Science Department, July 1999.
[64]. Ying Tan, A support vector machine with a Hybrid kernel and minimal VC dimension, IEEE transactions on knowledge and data engineering, Vol. 16, No. 4, April 2004.
[65]. Hua-Ping Zhang, Hong-Kui Yu, De-Yi Xiong, and Qun Liu, 2003. HHMM-based Chinese Lexical Analyzer ICTCLAS. 2nd SIGHAN workshop affiliated with 41st ACL, 184-187.
[66]. Jeffrey L. Solka, 2008. Text Data Mining: Theory and Methods. Statistics Surveys, Vol.2, 94-112.
[67]. L. T. N.P. Hughes, Novel signal shape descriptors through wavelet transforms and dimensionality reduction, Wavelet Applications in Signal and Image Processing X, (2003) 763-773.
[68]. N. C. J. Shawe-Taylor, Kernel Methods for Pattern Analysis, 2004.
[69]. A. J. C. C. Chatfield, Introduction to Multivariate Analysis, 1980.
[70]. W. B. M.S. Venkatarajan, New quantitative descriptors of amino acids based on multidimensional scaling of a large number of physicalchemical properties, Journal of Molecular Modeling, vol. 7, no. 12, (2004) 445-453.
[71]. M. J. M. O.C. Jenkins, Deriving action and behavior primitives from human motion data, International Conference on Intelligent Robots and Systems, vol. 3, 2002, pp. 2551-2556.
[72]. I. Y. B. Raytchev, K. Sakaue, Head pose estimation by nonlinear manifold learning, Proceedings of the 17th ICPR, 2004, pp. 462-466.
[73]. M. Penrose, Random Geometric Graphs, 2003.
[74]. L. K. S. S.T. Roweis, Nonlinear dimensionality reduction by Locally Linear Embedding, Science, vol. 290, no. 5500, (2000) 2323-2326.
[75]. E. Kokiopoulou, and Y. Saad, Orthogonal neighborhood preserving projections: A projection-based dimensionality reduction technique, Ieee Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 12, (2007) 2143-2156.
[76]. M. Meytlis, and L. Sirovich, On the dimensionality of face space, Ieee Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 7, (2007) 1262-1267.
[77]. H. Hotelling, Analysis of a complex of statistical variables into principal components, Journal of Educational Psychology, vol. 24, (1933) 417-441.
[78]. B. Li, C. H. Zheng, and D. S. Huang, Locally linear discriminant embedding: An efficient method for face recognition, Pattern Recognition, vol. 41, no. 12, (2008) 3813-3821.
[79]. H. F. Li, T. Jiang, and K. S. Zhang, Efficient and robust feature extraction by maximum margin criterion, Ieee Transactions on Neural Networks, vol. 17, no. 1, (2006) 157-165.
[80]. H.-K. Y. Hua-Ping Zhang, De-Yi Xiong, Qun Liu, HHMM-based Chinese Lexical Analyzer ICTCLAS, 2nd SIGHAN workshop affiliated with 41st ACL, 2003, pp. 184-187.
[81]. M. M. G. Salton, Introduction to Modern Information Retrieval, 1983.
[82]. D. M. M. Grobelnik, Approaching Analysis of EU IST Projects Database, International Conference on Information and Intelligent Systems, 2002.
[83]. D. G. Hopper, D.G. Haralson, M. Simpson, Review of ultra-resolution (10-100 megapixels) visualization systems built bytiling commercial display components, SPIE proceedings series vol. 4712, 2002, pp. 282-299.
[84]. K. Lagus, S. Kaski, and T. Kohonen, Mining massive document collections by the WEBSOM method, Information Sciences, vol. 163, no. 1-3, (2004) 135-156.
[85]. J. A. Wise, The ecological approach to text visualization, Journal of the American Society for Information Science, vol. 50, no. 13, (1999) 1224-1233.
[86]. Y. Guo, J. B. Gao, and P. W. Kwan, Twin Kernel Embedding, Ieee Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 8, (2008) 1490-1495.
[87]. R.Herbrich, T. G., P.Bollmann-Sdorra, and K.Obermayer, 1998. Learning Preference Relations for Information Retrieval. Proc. Int’l Conf. Machine Learning Workshop Learning for Text Categorization, 80-84.
[88]. T.Joachims, 2002. Optimizing Search Engines Using Clickthrough Data. Proc. Eight ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining, 133-142.
[89]. C. Burges, T. S., E. Renshaw, A. Lazier, M. Deeds, N. Hamilton and G. Hullender, 2005. Learning to Rank Using Gradient Descent. Proc. 22nd Int’l Conf. Machine Learning.
[90]. Y. Freund, R. I., and R. Schapire, 2003. An Efficient Boosting Algorithm for Combining Preferences. J. Machine Learning Research 4, 933-969.
[91]. O. Dekel, C. M., and Y. Singer, 2004. Log-Linear Models for Label Ranking. Advances in Neural Information Processing Systems 16.
[92]. Ghahramani, W. C. a. Z., 2005. Preference Learning with Gaussian Processes. Proc. 22nd Int’l Conf. Machine Learning, 137-144.
[93]. G. Fung, R. R., and B. Krishnapuram, 2006. Learning Rankings via Convex Hull Separation. Advances in Neural Information Processing Systems18.
[94]. Hauptmann, R. Y. a. A., 2006. Efficient Margin-Based Rank Learning Algorithms for Information Retrieval. Proc. Int’l Conf. Image and Video Retrieval.
[95]. C. Burges, R. R., and Q. Le, 2007. Learning to Rank with Nonsmooth Cost Functions. Advances in Neural Information Processing Systems 19.
[96]. Singer, K. C. a. Y., 2002. Pranking with Ranking. Advances in Neural Information Processing Systems 16 14, 641-647.
[97]. Harrington, E. F., 2003. Online Ranking/Collaborative Filtering Using the Perceptron Algorithm. Proc. 20th Int’l Conf. Machine Learning.
[98]. R. Caruana, S. B., and T. Mitchell, 1995. Using the Future to‘Sort Out’the Present: Rankprop and Multitask Learning for Medical Risk Evaluation. Advances in Neural Information Processing Systems 16.
[99]. Vikas C. Raykar, R. D., and Balaji Krishnapuram, 2008. A Fast Algorithm for Learning a Ranking Function from Large-Scale Data Sets. IEEE transactions on pattern analysis and machine intelligence 30(7), 1158-1170.
[100]. T. Joachims, 2005. A Support Vector Method for Multivariate Performance Measures, Proceedings of the International Conference on Machine Learning (ICML).
[101]. J. Diez, G. F. Bayon, J. R. Quevedo, J. J. del Coz, O. Luaces, J. Alonso, and A. Bahamonde, 2004. Discovering relevancies in very difficult regression problems: applications to sensory data analysis. In Proceedings of the 16th. European Conference on Artificial Intelligence (ECAI '04), 993-994.
[102]. Wilcoxon, F., 1945. Individual Comparisons by Ranking Methods. Biometrics Bull 1( 6), 80-83.
[103]. R.Herbrich, T. Graepel, and K Obermayer, 2000. Large Margin Rank Boundaries for Ordinal Regression. Advances in Large MarginClassifiers. 115-132.
[104]. Hua-Ping Zhang, Hong-Kui Yu, De-Yi Xiong, and Qun Liu, 2003. HHMM-based Chinese Lexical Analyzer ICTCLAS. 2nd SIGHAN workshop affiliated with 41st ACL, 184-187.
[105]. Jeffrey L. Solka, 2008. Text Data Mining: Theory and Methods. Statistics Surveys, Vol.2, 94-112.
[106]. Page, Lawrence,Brin, Sergey, Motwani, Rajeev and Winograd, Terry (1999). The PageRank citation ranking: Bringing order to the Web.
[107]. MacQueen, J., Some methods for classification and analysis of multivariate observations. Proc. Fifth Berkeley Symp. on Math. Statist. and Prob., 1967. 1: p. 281-297.
[108]. Rousseeuw, P.J., Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math., 1987. 20: p. 53-65.
[109]. Kaufman, L., Rousseeuw, P.J., Finding Groups in Data. 1990.
[110]. T. Zhang, R.R., and M. Livny, BIRCH: an efficient data clustering method for very large databases. Proc. of 1996 ACM-SIGMOD Intl. Conf. on Management of Data, 1996: p. 103-114.
[111]. S. Guha, R.R., K. Shim, CURE:An efficient clustering algorithm for large databases. Proc. of 1998 ACM-SIGMOD Intl. Conf. on Management of Data, 1998: p. 73-84.
[112]. Guha, S., R. Rastogi, and K. Shim, Rock: A robust clustering algorithm for categorical attributes. Information Systems, 2000. 25(5): p. 345-366.
[113]. Karypis, G., E.H. Han, and V. Kumar, Chameleon: Hierarchical clustering using dynamic modeling. Computer, 1999. 32(8): p. 68-75.
[114]. M. Ester, H.-P.K., J. Sander, X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise. Proc. of the 2nd Intl. Conf. on Knowledge Discovery and Data Mining, 1996: p. 226-231.
[115]. Ankerst, M., et al. OPTICS: Ordering points to identify the clustering structure. 1999. Philadelphia, Pa.
[116]. Agrawal, R., et al., Automatic subspace clustering of high dimensional data. Data Mining and Knowledge Discovery, 2005. 11(1): p. 5-33.
[117]. Perkowitz, M. and O. Etzioni. Category translation: Learning to understand information on the Internet. 1995. Montreal, Canada: Morgan Kaufmann Pub Inc.
[118]. R. A. Jarvis, E.A.P., Clustering using a similarity measure based on shared nearest neighbors. IEEE Transactions on Computers, 1973: p. 1025-1034.
[119]. Levent Ertoz , M.S., Vipin Kumar, A new shared nearest neighbor clustering algorithm and its applications. In: Workshop on Clustering High Dimensional Data and its Applications at 2nd SIAM International Conference on Data Mining, 2001.
[120]. Ertoz, L., M. Steinbach, and V. Kumar. Finding clusters of different sizes, shapes, and densities in noisy, high dimensional data. 2003. San Francisco, Ca.
[121]. Jansen, Spink, Saracevic. Real Life, Real Users, and Real Needs: A Study and Analysis of User Queries on the Web,Information Processing and Management, 2000, 36(2): 207– 227.
[122]. Robert, Bruce. Lexical Ambiguity and Information Retrieval,Information Systems, 1992, 10(2): 115–141.
[123]. Tanudjaja, Persona. A Contextualized and Personalized Web Search,In: Proc of the 35th Annual Hawaii International Conference on System Sciences, 2002.
[124]. Wang Ru-Jing, Teng Ming-Gui. A Generalized Spatial Linear Regression Model for Spatial Prediction , Pattern Recognition and Artificial Intelligence, 2005, 18(6): 708-712.
[125].王儒敬,葛运建,张晓明.基于粗糙集的空间对象分类学习算法.中国科学技术大学学报, 2006(1): 163-169。
[126]. Manber, Patel, Robison. Experience with Personalization on Yahoo! Communications of the ACM, 2000, 43(8): 35–39.
[127]. Michael. Adaptive web search: evolving a program that finds information,IEEE intelligent systems, 2006, 21(5): 72-77.
[128].张卫丰,徐宝文.基于遗传算法的搜索引擎调度,微电子学与计算机, 2001(4): 34-38。
[129]. Barabasi. Emergence of Scaling in Random Networks,Science, 1999(286): 509-512.
[130]. Stanley. Introduction to Phase Transitions and Critical Phenomena [M],Oxford University Press, New York, 1971.
[131]. Michael Chau. Design and Evaluation of a Multi-Agent Collaborative Web Mining System, Decision Support Systems, 2003, 35(1): 167-183.
[132]. Siti Narkhadijah. Multi-Agent Crawling System Architecture for Effective Web Retrieval, Postgraduate Annual Research Seminar, 2007.
[133]. Bernard, Jansen. Determining the Informational,Navigational and Transactional Intent of Web Queries. Information Processing and Management, 2008: 1251–1266.
[134].李宏亮,党岗。复杂自适应系统的描述及其分布仿真框架。计算机研究与发展,第39卷第10期, 2002(10): 1349-1354。
[135].胡淑礼.模糊数学及其应用。四川大学出版社,成都, 1994。
[136]. Tan Pang-Ning,Michael.数据挖掘导论。人民邮电出版社,北京,2006。