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\title{An Enhanced Web Data Learning Method for Integrating Item, Tag and Value for Mining Web Contents}
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             \author[1]{  Kavipriya.P}

             \affil[1]{  Dr.SNS Rajalakshmi College of arts and science.,CMS College of Science and Commerce.,Coimbatore.}

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\date{\small \em Received: 9 December 2012 Accepted: 5 January 2013 Published: 15 January 2013}

\maketitle


\begin{abstract}
        


The Proposed System Analyses the scopes introduced by Web 2.0 and collaborative tagging systems, several challenges have to be addressed too, notably, the problem of information overload. Recommender systems are among the most successful approaches for increasing the level of relevant content over the "noise." Traditional recommender systems fail to address the requirements presented in collaborative tagging systems. This paper considers the problem of item recommendation in collaborative tagging systems. It is proposed to model data from collaborative tagging systems with three-mode tensors, in order to capture the three-way correlations between users, tags, and items. By applying multiway analysis, latent correlations are revealed, which help to improve the quality of recommendations. Moreover, a hybrid scheme is proposed that additionally considers content-based information that is extracted from items.We propose an advanced data mining method using SVD that combines both tag and value similarity, item and user preference. SVD automatically extracts data from query result pages by first identifying and segmenting the query result records in the query result pages and then aligning the segmented query result records into a table, in which the data values from the same attribute are put into the same column. Specifically, we propose new techniques to handle the case when the query result records based on user preferences, which may be due to the presence of auxiliary information, such as a comment, recommendation or advertisement, and for handling any nested-structure that may exist in the query result records.

\end{abstract}


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\let\tabcellsep& 	 	 		 \par
The term is a buzzword, and is frequently misused to mean any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) but is also generalized to any kind of computer decision support system, including artificial intelligence, machine learning, and business intelligence. In the proper use of the word, the key term is discovery, commonly defined as "detecting something new". Even the popular book "Data mining: Practical machine learning tools and techniques with Java" (which covers mostly machine learning material) was originally to be named just "Practical machine learning", and the term "data mining" was only added for marketing reasons. Often the more general terms "(large scale) data analysis", or "analytics" -or when referring to actual methods, artificial intelligence and machine learning -are more appropriate. According to one source, data mining is a marketing term coined by HNC, a San Diego-based company (now merged into FICO), at the beginning of the century to pitch their Data Mining Workstation.\par
The actual data mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection) and dependencies (association rule mining). This usually involves using database techniques such as spatial indexes. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting are part of the data mining step, but do belong to the overall KDD process as additional steps. 
\section[{b) Web Mining i. Web Structure Mining}]{b) Web Mining i. Web Structure Mining}\par
Web structure mining is the process of using graph theory to analyze the node and connection structure of a web site. According to the type of web ata mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD), is a field at the intersection of computer science and statistics, is the process that attempts to discover patterns in large data sets. It utilizes methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data preprocessing, model and inference considerations, interestingness metrics, complexity considerations, D R. Marutha Veni ? \& P. Kavipriya ? structural data, web structure mining can be divided into two kinds: 1. Extracting patterns from hyperlinks in the web: a hyperlink is a structural component that connects the web page to a different location. 2. Mining the document structure: analysis of the treelike structure of page structures to describe HTML or XML tag usage. 
\section[{ii. Web Content Mining}]{ii. Web Content Mining}\par
Mining, extraction and integration of useful data, information and knowledge from Web page contents. The heterogeneity and the lack of structure that permeates much of the ever expanding information sources on the World Wide Web, such as hypertext documents, makes automated discovery, organization, and search and indexing tools of the Internet and the World Wide Web such as Lycos, Alta Vista, WebCrawler, ALIWEB, MetaCrawler, and others provide some comfort to users, but they do not generally provide structural information nor categorize, filter, or interpret documents. In recent years these factors have prompted researchers to develop more intelligent tools for information retrieval, such as intelligent web agents, as well as to extend database and data mining techniques to provide a higher level of organization for semi-structured data available on the web. The agent-based approach to web mining involves the development of sophisticated AI systems that can act autonomously or semiautonomously on behalf of a particular user, to discover and organize web-based information. Different from most existing studies on Web session identification, a novel dynamic real time user session processes description method is presented in this paper. The proposed scheme doesn't rely on presupposed threshold or client/server side data which are widely used in traditional session detection approaches. A new parameter is defined based on interarrival time of HTTP requests. A nonlinear algorithm is introduced for quantization. Nonparametric hidden semi-Markov model is applied to distinguish the user session processes. A probability function is derived for predicting user session processes. Experiments based on real HTTP traces of large-scale Web proxies are implemented to valid the proposal. 
\section[{II.}]{II.} 
\section[{Literature Review}]{Literature Review}\par
ii. Drawbacks of the Proposal Different from traditional user session model, in this paper, a user session of a special user is divided into three segments: activity period, silent period and off-lining period. Activity period means the user is surfing the Internet, which causes the frequent interactions between user and different remote servers. Silent period indicates network connection is enable, but the user does nothing. During this period, the HTTP requests are mainly launched by softwares instead of user's own action. Thus, the number of requests in this period is far less than that of activity period. The last period means the network connection is unworkable or user has left. 
\section[{b) Applying Concept Analysis to User-Session-Based}]{b) Applying Concept Analysis to User-Session-Based}\par
Testing of Web Applications i. Proposal\par
The continuous use of the Web for daily operations by businesses, consumers, and the government has created a great demand for reliable Web applications. One promising approach to testing the functionality of Web applications leverages the user session data collected by Web servers. User-sessionbased testing automatically generates test cases based on real user profiles. The key contribution of this paper is the application of concept analysis for clustering user sessions and a set of heuristics for test case selection. Existing incremental concept analysis algorithms are exploited to avoid collecting and maintaining large usersession data sets and to thus provide scalability. We have completely automated the process from user session collection and test suite reduction through test case replay. Our incremental test suite update algorithm, coupled with our experimental study, indicates that concept analysis provides a promising means for incrementally updating reduced test suites in response to newly captured user sessions with little loss in fault detection capability and program coverage.\par
ii. Drawbacks of Proposal One approach to testing the functionality of Web applications that addresses the problems of the path based approaches is to utilize capture and replay mechanisms to record user-induced events, gather and convert them into scripts, and replay them for testing, Tools such as Web King and Rational Robot provide automated testing of Web applications by collecting data from users through minimal configuration changes to the Web server. The recorded events are typically base requests and name-value pairs (for example, form field data) sent as requests to the Web server. A base request for a Web application is the request type and resource location without the associated data. To our knowledge, these techniques do not include incremental approaches to test suite reduction. 
\section[{c) Clustering and Tailoring User Session Data for}]{c) Clustering and Tailoring User Session Data for}\par
Testing Web Applications i. Proposal Web applications have become major driving forces for world business. Effective and efficient testing of evolving web applications is essential for providing reliable services. In this paper, we present a user session based testing technique that clusters user sessions based on the service profile and selects a set of representative user sessions from each cluster. Then each selected user session is tailored by augmentation with additional requests to cover the dependence relationships between web pages. The created test suite not only can significantly reduce the size of the collected user sessions, but is also viable to exercise fault sensitive paths. We conducted two empirical studies to investigate the effectiveness of our approach one was in a controlled environment using seeded faults, and the other was conducted on an industrial system with real faults. The results demonstrate that our approach consistently detected the majority of the known faults by using a relatively small number of test cases in both studies.\par
ii. Drawbacks of the Proposal User-session based testing makes use of field data to create test cases, which has the great potential to efficiently generate test cases that can effectively detect residual faults. However, this approach is relatively new compared to traditional well developed techniques. There are several issues that must be addressed before it can serve as a sole testing method in practice. For an application that has been in production for a long time, the number of user sessions can be extremely large. Using all of the collected user session data requires much effort to determine which portion of the data can serve as the best representative of the system behavior. Nevertheless, a vast number of user sessions may not necessarily guarantee good coverage of the expected system behavior. 
\section[{d) Separating Interleaved User Sessions from Web Log i. Proposal}]{d) Separating Interleaved User Sessions from Web Log i. Proposal}\par
Analysis of user behavior on the Web presupposes a reliable reconstruction of the users' navigational activities. The quality of reconstructed sessions affects the result of Web usage mining. This paper presents a new approach for interleaved server session from Web server logs using m-order Markov model combined with a competitive algorithm. The proposed approach has the ability to reconstruct interleaved sessions from server logs. This capability makes our work distinct from other session reconstruction methods. The experiments show that our approach provides a significant improvement in regarding interleaved sessions compared to the traditional methods. 
\section[{ii. Drawbacks of the Proposal}]{ii. Drawbacks of the Proposal}\par
Session reconstruction is an essential data preprocess step in Web usage mining. The primary session reconstruction approaches based on time and reference cannot reconstruct the interleaved sessions and perform poorly when the client's IP address is not available. In this paper, an algorithm based on m-order Markov model is proposed which can reconstruct interleaved sessions from Web logs. The experiments show the promising result that the m-order Markov model has the ability to divide interleaved sessions, and can provide a further improvement when it is combined with the competitive approach. 
\section[{e) Optimal Algorithms for Generation of User Session}]{e) Optimal Algorithms for Generation of User Session}\par
Sequences Using Server Side Web User Logs i. Proposal\par
Identification of user session boundaries is one of the most important processes in the web usage mining for predictive prefetching of user next request based on their navigation behavior. This paper presents new techniques to identify user session boundaries by considering IPaddress, browsing agent, intersession and intrasession timeouts, immediate link analysis between referred pages and backward reference analysis without searching the whole tree representing the server pages. A complete set of user session sequences and the learning graph based on these user session sequences is also generated. Using this graph predictive prefetching is done. Comparison on the performance of the given approach with the existing reference length method and maximal reference method was done. Our analysis with different server's logs shows that our approach provides better results in terms of time complexity and precision to identify user session boundaries and also to generate all the relevant user session sequences. 
\section[{ii. Drawbacks of Proposal}]{ii. Drawbacks of Proposal}\par
The analysis indicated that the existing web technology faces so many problems. One among them is personalization of web pages. Personalization is achieved if we know the browsing pattern of users. Our algorithm generates the efficient user session sequences with the less time complexity and good accuracy compared to the existing works. Thus we can reduce the latency. In the forth-coming papers USIDALG can be modified to generate the efficient learning graph to predict and prefect the user's next request. 
\section[{iii. The Existing Researches}]{iii. The Existing Researches}\par
The Existing researches in personalizing the web user were single entity based and a summary of few researches are presented and the proposed system is developed by clearly understanding the below problems. This chapter discusses some of the existing techniques presented by different authors. ( D D D D D D D D )\par
common usage in widely used operating systems, so we restrict our discussion here to common hierarchical methods. Hierarchical storage was first introduced to end-users in the Multics operating system in the mid 60s. Users were allocated a personal directory, in which they could create their own subdirectories, sub-subdirectories, etc., and store their files in any of these "locations." This directory structure was later applied in the Unix and the Linux operating systems. Deeds, N. Hamilton, and G. Hullender, proposed Search as an Alternative to Navigation Through most of its long history, the hierarchical method has met with criticism. One disadvantage is that classification of information can 'hide' it from the user, and therefore reduce the chances of quick retrieval or reminding. In addition, the act of categorisation is itself cognitively challenging; users may find it hard to categorise information that could be stored in more than one category. Categorisation is also difficult because it requires that people anticipate future usage; moreover, that usage may change over time. At retrieval time, users need to recall how information was classified, which can be difficult when there are multiple categorisation possibilities. These problems were illustrated in a study of email categorisation. They found that users with many categories found it harder to file, and were more likely to create spurious unused folders. These apparent problems with navigation caused many PIM researchers and software developers to turn to Search as an alternative. There are intuitive potential advantages of search for both retrieval and organization. Search promises to be more flexible and efficient at retrieval, it does not depend on remembering the correct storage location; instead, users can specify in their query any attribute they happen to remember. They can also retrieve information via a single query instead of using multiple operations to laboriously navigate to the relevant part of their folder hierarchy. Regarding storage, search potentially finesses the organizational problem -as users don't have to engage in complex organizational strategies that exhaustively anticipate their future retrieval requirements. Retrieval: Search is more efficient and flexible for retrieval, thus improved quality of search engines should lead to a substantial increase in file search and eventually a preference for search over navigation.\par
File Organization: Users are known to have problems organizing files effectively for retrieval. Search allows retrieval without such manual organization and improved search should lead to a reduced use of filing strategies in preparation for later retrieval.\par
16. Q. Gan, J. Attenberg, A. Markowetz, and T. Suel worked on a Navigation or Search: Prior Evidence Pertaining to the Debate Evidence concerning users' search preference comes from empirical studies that examine retrieval behaviour. An early paper concerning users' retrieval habits, combined Barreau's interviews of novice personal computer users (using DOS, Windows 3.1 and OS/2) with Nardi's interviews of experienced Macintosh users. In both cases, users "overwhelmingly" preferred to navigate to their files than to search for them. Similar preferences for navigation were obtained in other more recent studies. These early findings raise a question-if search better suits users' requirements, why do they prefer navigation? One argument is that search technology is still immature. For example, Fertig and his colleagues argued that these navigation preferences result from limitations in search technology, and that improvements in search would inevitably lead to the replacement of navigation. They noted that the PIM search engines of that time (the mid 90s) were "slow, difficult, or only operate on file names (not content)" and did not provide incremental indexing. Fertig et al. further speculated that "inclusion of these better search techniques into current systems could sway results". However, their claim that the improvement of search engines would lead to an increased preference for search over navigation has not been tested empirically. 17. T. Joachims, presented few Other evidence challenging the effects of improved search concerns users' organizational efforts to prepare for future retrieval. There is some evidence that users seem to want to preserve folders, even when improved search is possible. Jones, Phuwanartnurak, Gill, \& Bruce asked \hyperref[b14]{[14]} participants the following question: "Suppose you could find your personal information using a simple search rather than your current folders.? Can we take your folders away?" Only one participant responded positively. In contrast, Dumais et al.'s participants tended to mildly agree with the sentence "I would likely to put less effort into maintaining a detailed set of folders for my files if I could depend on SIS (i.e., the Stuff I've Seen search engine) to find what I am looking for". Both studies asked whether the use of improved search engines would lead to less reliance on folders, but  {\ref (} The three types of recommendations in STSs (i.e., item, tag, and user recommendations) have been so far addressed separately by various approaches, which differ significantly to each other and have, in general, an ad hoc nature. Since in STSs all three types of recommendations are important, what is missing is a unified framework that can provide all recommendation types with a single method. Moreover, existing algorithms do not consider the three dimensions of the problem. In contrast, they split the threedimensional space into pair relations \{user, item\}, \{user, tag\}, and \{tag, item\}, that are two-dimensional, in order to apply already existing techniques like CF, link mining, etc. Therefore, they miss a part of the total interaction between the three dimensions. What is required is a method that is able to capture the three dimensions all together without reducing them into lower dimensions.\par
Finally, the existing approaches fail to reveal the latent associations between tags, users, and items. Latent associations exist due to three reasons: As an example, assume two users in an STSs for Web bookmarks (e.g., Del.icio.us, Bibsonomy). The first user is a car fan and tags a site about cars, whereas the other tags a site about wild cats. Both use the tag "jaguar." When they provide the tag "jaguar" to retrieve relevant sites, they will receive both sites (cars and wild cats). Therefore, what is required is a method that can discover the semantics that are carried by such latent associations, which in the previous example can help to understand the different meanings of the tag "jaguar." III. 
\section[{Methodology a) Singular Value Decomposition}]{Methodology a) Singular Value Decomposition}\par
Let X denote an m x n matrix of real-valued data and rank r, where without loss of generality m?n, and therefore r ? n. In the case of microarray data, x ij is the expression level of the i th gene in the j th assay. The elements of the i th row of X form the n-dimensional vector g i , which we refer to as the transcriptional response of the i th gene. Alternatively, the elements of the j th column of X form the m-dimensional vector a j , which we refer to as the expression profile of the j th assay.\par
The equation for singular value decomposition of X is the following:\par
(5.1)\par
Where U is an m x n matrix, S is an n x n diagonal matrix, and V T is also an n x n matrix. The columns of U are called the left singular vectors, \{u k \}, and form an orthonormal basis for the assay expression profiles, so that u i ?u j = 1 for i = j, and u i ?u j = 0 otherwise. The rows of V T contain the elements of the right singular vectors, \{v k \}, and form an orthonormal basis for the gene transcriptional responses. The elements of S are only nonzero on the diagonal, and are called the singular values. Thus, S = diag(s 1 ,...,s n ). Furthermore, s k > 0 for 1 ? k ? r, and s i = 0 for (r +1) ? k ? n. By convention, the ordering of the singular vectors is determined by high-to-low sorting of singular values, with the highest singular value in the upper left index of the S matrix. Note that for a square, symmetric matrix X, singular value decomposition is equivalent to diagonalization, or solution of the eigenvalue problem.\par
One important result of the SVD of X is that (5.2) is the closest rank-l matrix to X. The term "closest" means that X (l) minimizes the sum of the squares of the difference of the elements of X and X (l ) , ? ij |x ij -x (l ) ij | 2 . One way to calculate the SVD is to first calculate V T and S by diagonalizing X T X:\par
(5.3) and then to calculate U as follows:\par
(5.4)\par
where the (r+1),...,n columns of V for which s k = 0 are ignored in the matrix multiplication of Equation  {\ref 5}.4.\par
Choices for the remaining n-r singular vectors in V or U may be calculated using the Gram-Schmidt orthogonalization process or some other extension method. In practice there are several methods for calculating the SVD that are of higher accuracy and speed. Section 4 lists some references on the mathematics and computation of SVD.\par
IV. 
\section[{Implementation and Findings}]{Implementation and Findings}\par
Given two data values f1 and f2 from different QRRs, we require their similarity, s12, to be a real value in [0, 1]. The data value similarity is calculated according to the data type tree shown in Fig.  {\ref 4}. Each child node is a subset of its parent node. For example, the "string" type includes several children data types, which are common on the Web such as "datetime", "float" and "price". The maximum depth of the data type tree is 4. In the following, we will refer to a non-string data type as a specific data type. Given two data values f1 and f2, we first judge their data types and then fit them as deeply as possible into the nodes n1 and n2 of the data type tree. For example, given a string "784", we will put it in node "integer".\par
The similarity s12 between two data values f1 and f2 with data type nodes n1 and n2 is defined as: where p(ni) refers to the parent node of ni in the data type tree. The similarity between data values f1 and f2 is set to:  ? string cosine similarity of f1 and f2, if both f1 and f2 belong to the string data type. ? 0 otherwise, which occurs when one of f1 and f2 belongs to the string data type and the other one belongs to a specific data type, or f1 and f2 belong to different specific data types without any direct parent.\par
V. In this paper, we developed a unified framework to model the three types of entities that exist in a social tagging system: users, items, and tags. We examined multiway analysis on data modeled as 3-order tensor, to reveal the latent semantic associations between users, items, and tags. The multiway latent semantic analysis and dimensionality reduction is performed by combining the HOSVD method with the Kernel-SVD smoothing technique. Our approach improves recommendations by capturing users multimodal of item/tag/user. Moreover, we study a problem of how to provide user recommendations, which can have significant applications in real systems but which have not been studied in depth so far in related research. We also performed experimental comparison of the proposed method against state-of the-art recommendations algorithms, with two real data sets (Last.fm and BibSonomy). Our results show significant improvements in terms of effectiveness measured through recall/precision. As future work, we intend to examine different methods for extending SVD to high-order tensors such as the Parallel Factor Analysis. We also indent to apply different weighting methods for the initial construction of a tensor. A different weighting policy for the tensor's initial values could improve the overall performance of our approach. b) Future Enhancements Although SVD has been shown to be an accurate data extraction method, it still suffers from some limitations. First, it requires at least two QRRs in the query result page. Second, any optional attribute that appears as the start node in a data region will be treated as auxiliary information. Third, similar to other related works, SVD mainly depends on tag structures to discover data values. Therefore, Finally, as previously mentioned, if a query result page has more than one data region that contains result records and the records in the different data regions are not similar to each other, then SVD will select only one of the data regions and discard the others.  
\section[{Conclusion \& Future Enhancements}]{Conclusion \& Future Enhancements}\begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-2.png}
\caption{\label{fig_0}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-3.png}
\caption{\label{fig_2}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{13}\includegraphics[]{image-4.png}
\caption{\label{fig_4}1 . 3 .}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-5.png}
\caption{\label{fig_5}?}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-6.png}
\caption{\label{fig_7}}\end{figure}
 			\footnote{© 2013 Global Journals Inc. (US)} 			\footnote{EAn Enhanced Web Data Learning Method for Integrating Item, Tag and Value for Mining Web Contents} 			\footnote{EAn Enhanced Web Data Learning Method for Integrating Item, Tag and Value for Mining Web Contents} 			\footnote{© 2013 Global Journals Inc. (US) Global Journal of Computer Science and Technology} 			\footnote{EAn Enhanced Web Data Learning Method for Integrating Item, Tag and Value for Mining Web Contents} 		 		\backmatter  			 \par
eighteenth annual ACM symposium on Theory of computing, pp.  {\ref [136]} {\ref [137]} {\ref [138]} {\ref [139]} {\ref [140]} {\ref [141]} {\ref [142]} {\ref [143]} {\ref [144]} {\ref [145]} {\ref [146]} {\ref 1986}. 15. D. Gusfield, Algorithms on Strings, Trees, and Sequences: Computer Science and Computational Biology. Cambridge University Press, 1997.\par
16. B. Liu, W. S. Lee, P. S. Yu, and X. Li proposed a Faster retrieval method for Improved search engines are substantially faster than old ones. In some cases they have been demonstrated to be 1000 times faster.\par
User-centred design : Choosing between formats was not the only step the user had to take in older search engines. In addition, users had to choose between file name search or full text search, and also optionally specify the time the file was recently modified. To achieve a reasonable retrieval time, the user needed to input more information in order for the computer to do less, a feature which reflects a machine-oriented design. Newer search engines' retrieval speed allows them to reduce the query launching steps and complications to a minimum.\par
Incremental Search : One advantage of newer search engines is that they support incremental search, so that the search begins as soon as the user types the first character of the query. This has the benefit of being interactive: allowing users to refine their query in light of the results returned, and truncate the query after typing just a few characters if the target item is already in view. Older search engines were less efficient: prompting the user via form filling to specify multiple attribute fields and hit carriage return before the query is sent off. Incrementality, according to Raskin, has several advantages: (a) user and computer do not have to wait for each other, (b) users know they have typed enough to disambiguate their query because the desired file appears in the display, (c) users receive constant feedback as to the results of the search -they can correct spelling mistakes or refine search words without interrupting the search. 17. W. Ng, L. Deng, and D. L. Lee, proved that given these improvements in desktop search engines, it is now time to examine their implications: What are users' file retrieval preferences, what motivates retrieval by search, and what is the effect of improved desktop search engines on file retrieval preferences and file organization? If the availability of these improved desktop search engines leads to a substantial increase in search, then it is reasonable to assume that this effect will continue to grow as search engines improve. If, on the other hand, no such effect is found, it raises questions regarding claims that improved search engines affect retrieval preferences and file organization, though it always can be claimed that future improvements in search could change this. As search engines are consistently improving and will continue to do so, the examination of their implications on PIM should be a continuous effort.			 			  				\begin{bibitemlist}{1}
\bibitem[Cohen et al. ()]{b9}\label{b9} 	 		‘A Flexible Learning System for Wrapping Tables and Lists in HTML Documents’.  		 			W Cohen 		,  		 			M Hurst 		,  		 			L Jensen 		.  	 	 		\textit{Proc. 11th World Wide Web Conf},  				 (11th World Wide Web Conf)  		2002. p. .  	 
\bibitem[Buttler et al. ()]{b5}\label{b5} 	 		‘A Fully Automated Object Extraction System for the World Wide Web’.  		 			D Buttler 		,  		 			L Liu 		,  		 			C Pu 		.  	 	 		\textit{Proc. 21st Int'l Conf. Distributed Computing Systems},  				 (21st Int'l Conf. Distributed Computing Systems)  		2001. p. .  	 
\bibitem[Goldberg and Tarjan]{b14}\label{b14} 	 		‘A new approach to the maximum flow problem’.  		 			A V Goldberg 		,  		 			R E Tarjan 		.  	 	 		\textit{Proceedings of the},  				 (the)  		 	 
\bibitem[Cohen and Jensen ()]{b10}\label{b10} 	 		‘A Structured Wrapper Induction System for Extracting Information from Semi-Structured Documents’.  		 			W Cohen 		,  		 			L Jensen 		.  	 	 		\textit{Proc. IJCAI},  				 (IJCAI)  		2001.  	 
\bibitem[Chen et al. ()]{b8}\label{b8} 	 		‘Automatic Composite Wrapper Generation for Semi-Structured Biological Data Based on Table Structure Identification’.  		 			L Chen 		,  		 			H M Jamil 		,  		 			N Wang 		.  	 	 		\textit{SIGMOD Record}  		2004. 33  (2)  p. .  	 
\bibitem[Embley et al. ()]{b13}\label{b13} 	 		‘Conceptual-model-based Data Extraction from Multiple-record Web Pages’.  		 			D W Embley 		,  		 			D M Campbell 		,  		 			Y S Jiang 		,  		 			S W Liddle 		,  		 			D W Lonsdale 		,  		 			Y.-K Ng 		,  		 			R D Smith 		.  	 	 		\textit{Data and Knowledge Engineering}  		1999. 31  (3)  p. .  	 
\bibitem[Breese et al. ()]{b4}\label{b4} 	 		‘Empirical Analysis of Predictive Algorithms for Collaborative Filtering’.  		 			J Breese 		,  		 			D Heckerman 		,  		 			C Kadie 		.  	 	 		\textit{Proc. Conf. Uncertainty in Artificial Intelligence},  				 (Conf. Uncertainty in Artificial Intelligence)  		1998. p. .  	 
\bibitem[Ali-Hasan and Adamic ()]{b1}\label{b1} 	 		‘Expressing Social Relationships on the Blog through Links and Comments’.  		 			N Ali-Hasan 		,  		 			A Adamic 		.  	 	 		\textit{Proc. Int'l Conf. Weblogs and Social Media (ICWSM)},  				 (Int'l Conf. Weblogs and Social Media (ICWSM))  		2007.  	 
\bibitem[Chang and Lui ()]{b7}\label{b7} 	 		‘IEPAD: Information Extraction Based on Pattern Discovery’.  		 			C H Chang 		,  		 			S C Lui 		.  	 	 		\textit{Proc. 10th World Wide Web Conf},  				 (10th World Wide Web Conf)  		2001. p. .  	 
\bibitem[Brand ()]{b3}\label{b3} 	 		‘Incremental Singular Value Decomposition of Uncertain Data with Missing Values’.  		 			M Brand 		.  	 	 		\textit{Proc. European Conf. Computer Vision (ECCV '02)},  				 (European Conf. Computer Vision (ECCV '02))  		2002.  	 
\bibitem[Crescenzi et al. ()]{b12}\label{b12} 	 		‘Roadrunner: Towards Automatic Data Extraction from Large Web Sites’.  		 			V Crescenzi 		,  		 			G Mecca 		,  		 			P Merialdo 		.  	 	 		\textit{Proc. 27 th Int'l Conf. Very Large Data Bases},  				 (27 th Int'l Conf. Very Large Data Bases)  		2001. p. .  	 
\bibitem[Chang et al. ()]{b6}\label{b6} 	 		‘Structured Databases on the Web: Observations and Implications’.  		 			K C Chang 		,  		 			B He 		,  		 			C Li 		,  		 			M Patel 		,  		 			Z Zhang 		.  	 	 		\textit{SIGMOD Record}  		2004. 33  (3)  p. .  	 
\bibitem[Acar and Yener (2009)]{b0}\label{b0} 	 		‘Unsupervised Multiway Data Analysis: A Literature Survey’.  		 			E Acar 		,  		 			B Yener 		.  	 	 		\textit{IEEE Trans. Knowledge and Data Eng}  		Jan. 2009. 21  (1)  p. .  	 
\bibitem[Berry et al. ()]{b2}\label{b2} 	 		‘Using Linear Algebra for Intelligent Information Retrieval’.  		 			M Berry 		,  		 			S Dumais 		,  		 			G O'brien 		.  	 	 		\textit{SIAM Rev}  		1994. 37  (4)  p. .  	 
\bibitem[Workshop Adaptive Text Extraction and Mining ()]{b11}\label{b11} 	 		\textit{Workshop Adaptive Text Extraction and Mining},  				2001.  	 
\end{bibitemlist}
 			 		 	 
\end{document}
