13552447
Reverse mapping of referral links from storage hierarchy for Web documents
Due to the lack of back-pointers, information about the referral parent(s) of a given Web page is not usually available to Web surfers. This significantly reduces the effectiveness of Web surfing and information discovery. We propose a mechanism to locate the referral parent(s) for a given fragment of a Web document (in terms of an URL address) from the client side. The basic idea is to explore the hierarchical hyperlink structure of a Web document fragment from its storage path directory information that is embedded in its URL. Two mechanisms were proposed: exhaustibility first search (EFS) and generalization first search (GFS). Results showed that through direct mapping of the storage hierarchy path to the URL address and then performing reverse tracing along the hyperlink structure, the chance of discovering a referral parent ranged from 41% to 51%. Compared to EFS, GFS was found to reduce the average search space from 26 pages to 4 pages.
19735283
A generalized site ranking model for Web IR
Normally, the unit for a ranking model in a Web IR system is a Web page, which is, sometimes, just an information fragment. A larger unit considering the linkage information may be desired to reduce the cognitive overload for users to identify the complete information from the interconnected Web. We propose a ranking model to measure the relevance of the whole Web site. We take some illustrations to show the idea and provide evidences to indicate its effectiveness.
12184758
Integrating program component executables on distributed memory architectures via MPH
Summary form only given. A growing trend in developing large and complex applications on today's Teraflop computers is to integrate stand-alone and/or semiindependent program components into a comprehensive simulation package. One example is the climate system model, which consists of atmosphere, ocean, land-surface and sea-ice. Each component is semiindependent and has been developed at different institutions. We study how this multicomponent multiexecutable application can run effectively on distributed memory architectures. We identify five effective execution modes and develop the MPH library to support application developments utilizing these modes. MPH performs component-name registration, resource allocation and initial component handshaking in a flexible way.
2829824
A Probabilistic Approach for Optimizing Spectral Clustering
Spectral clustering enjoys its success in both data clustering and semi-supervised learning. But, most spectral clustering algorithms cannot handle multi-class clustering problems directly. Additional strategies are needed to extend spectral clustering algorithms to multi-class clustering problems. Furthermore, most spectral clustering algorithms employ hard cluster membership, which is likely to be trapped by the local optimum. In this paper, we present a new spectral clustering algorithm, named "Soft Cut". It improves the normalized cut algorithm by introducing soft membership, and can be efficiently computed using a bound optimization algorithm. Our experiments with a variety of datasets have shown the promising performance of the proposed clustering algorithm.
270633
Web document clustering using hyperlink structures
WEB DOCUMENT CLUSTERING USING HYPERLINK STRUCTURES XIAOFENG HE y , HONGYUAN ZHA , CHRIS H.Q. DING y AND HORST D. SIMON y Abstract. With the exponential growth of information on the World Wide Web, there is great demand for developing e cient and e ective methods for organizing and retrieving the information available. Document clustering plays an important role in information retrieval and taxonomy man- agement for the World Wide Web and remains an interesting and challenging problem in the eld of web computing. In this paper we consider document clustering methods exploring textual infor- mation, hyperlink structure and co-citation relations. In particular, we apply the normalized-cut clustering method developed in computer vision to the task of hyperdocument clustering. We also explore some theoretical connections of the normalized-cut method to K-means method. We then experiment with normalized-cut method in the context of clustering query result sets for web search engines. Keywords. World Wide Web, graph partitioning, cheeger constant, clustering method, K-means method, normalized cut method, eigenvalue decomposition, power method. 1. Introduction. Currently the World Wide Web contains billions of documents and it is still growing rapidly. Finding the relevant documents to satisfy a user's infor- mation need is a very important and challenging task. Many commercial search en- gines have been developed and used by millions of people all over the world. However, the relevancy of documents returned in search engine result sets is still lacking, and further research and development is needed to really make search engines a ubiquitous information-seeking tool. The World Wide Web has a rich structure: it contains both textual web documents and the hyperlinks that connect them. The web documents and hyperlinks between them form a directed graph in which the web documents can be viewed as vertices and the hyperlinks as directed edges. Algorithms have been de- veloped utilizing this directed graph to extract information contained in a collection of hyperlinked web documents. Kleinberg proposed HITS algorithm based purely on hyperlink information to retrieve the most relevant information: authority and hub documents for a user query 20]. However, if the hypertext collection consists of sev- eral topics, authority and hub documents may only cover the most popular topics and leave out the less popular ones. One way to remedy this situation is to rst partition the hypertext collection into topical groups, and present the search results as a list of topics to the user. This leads to the need to cluster web documents based on both the textual and hyperlink information. There exists a large literature on clustering methods and algorithms 13, 19]. Gen- erally speaking, the purpose of cluster analysis is to organize the data into meaningful groups: the data objects in the same group are highly similar and those in di erent groups are dissimilar. Judging the e ectiveness of a clustering algorithm is di cult and usually application-dependent. In this paper, we apply a similarity-based cluster- ing method to the problem of clustering web documents. It utilizes a graph-theoretic criterion called normalized cut which has its root in the study of graph isoperimetric Department of Computer Science and Engineering, The Pennsylvania State University, Uni- versity Park, PA 16802, f xhe,zha g @cse.psu.edu . This work was supported in part by NSF grant CCR-9901986. y NERSC Division, Lawrence Berkeley National Laboratory, University of California, Berkeley, CA 94720, f xfhe,chqding,hdsimon g @lbl.gov . Supported by Department of Energy through an LBL LDRD fund.
10613301
An Optimal Index Reshuffle Algorithm for Multidimensional Arrays and Its Applications for Parallel Architectures
Reshuffling elements of a multidimensional array according to an index operation traditionally requires an auxiliary buffer of the same size as the original array. We describe a new in-place algorithm using vacancy tracking cycles with minimum memory access which eliminates the buffer array and the related copy-back, speeding up the reshuffle significantly for large arrays. The algorithm can be parallelized using a multithread approach on shared-memory multiprocessor computers. On distributed-memory multiprocessor computers, the index reshuffle of distributed multidimensional arrays amounts to a remapping of processor domains and is carried out using the in-place local algorithm combined with a global exchange algorithm. Implementation and test results on CRAY T3E and IBM SP indicate the effectiveness of the algorithm.
4990172
Structure search and stability enhancement of Bayesian networks
Learning Bayesian network structure from large-scale data sets, without any expert-specified ordering of variables, remains a difficult problem. We propose systematic improvements to automatically learn Bayesian network structure from data. (1) We propose a linear parent search method to generate candidate graph. (2) We propose a comprehensive approach to eliminate cycles using minimal likelihood loss, a short cycle first heuristic, and a cut-edge repairing. (3) We propose structure perturbation to assess the stability of the network and a stability-improvement method to refine the network structure. The algorithms are easy to implement and efficient for large networks. Experimental results on two data sets show that our new approach outperforms existing methods.