21949746
An Improved Usage-Based Ranking
A good ranking is critical to gain a positive searching experience. With usage data collected from past searching activities, it could be improved from current approaches which are largely based on text or link information. In this paper, we proposed a usage-based ranking algorithm. Basically, it calculates the rank score on time duration considering the propagated effect, which is an improvement on the simple selection frequency determined method. Besides, it also has some heuristics to further improve the accuracy of top-positioned results.
18215853
Study for fusion of different sources to determine relevance
The relevance of a Web document could be measured not only by its text content, but also by some other factors such as the link connectivity and usage patterns. In previous data fusion researches, the text is the only source to determine the relevance, and only the different runs (e.g. by different retrieval models, different query or document representations) on this same source are combined. It is the purpose of this paper to investigate whether the different sources can be combined to determine the relevance with a better accuracy than any single source. We conducted a preliminary experiment to test its feasibility and effectiveness and a positive result was obtained.
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.
9546816
A Framework of HTML Content Selection and Adaptive Delivery
In this paper, we propose a content selection and adaptive delivery framework to adapt content delivery to different client capabilities and preferences. The framework provides language support to content publishers with XML-based markup language to describe the content and control the transformation and delivery process. Automatic content classification and selective delivery based on match between content class and user capability or preference are also implemented. Thus, when content descriptions from server are not available, adaptive content delivery is still possible. The framework is generic and scalable. Performance studies on content classification and selection model show that it is effective in saving network resources and improving clients’ access experiences.
10076076
Quantitative Modeling for Web Objects' Cacheability
In this paper, we propose a mathematical model, called the E-Cacheability Index, to quantify and compare the effectiveness of caching web objects on Internet. Unlike previous related work on web object cacheability, our study is based on a comprehensive study of all object attributes related to the HTTP protocol transfer, content reuse, freshness re-validation as well as the inter-relationship/dependency among these attributes in the final caching decision. This model is important because it not only estimates the cacheability effectiveness of web content, but also provides hints on web caching optimization.
15790257
K-nearest-neighbor consistency in data clustering: incorporating local information into global optimization
Nearest neighbor consistency is a central concept in statistical pattern recognition, especially the kNN classification methods and its strong theoretical foundation. In this paper, we extend this concept to data clustering, requiring that for any data point in a cluster, its k-nearest neighbors and mutual nearest neighbors should also be in the same cluster. We study properties of the cluster k-nearest neighbor consistency and propose kNN and kMN consistency enforcing and improving algorithms. Extensive experiments on internet newsgroup datasets using the K-means clustering algorithm with kNN consistency enhancement show that kNN / kMN consistency can be improved significantly (about 100% for 1MN and 1NN consistencies) while the clustering accuracy is improved simultaneously. This indicates the local consistency information helps the global cluster objective function optimization.
1831347
Minimum redundancy feature selection from microarray gene expression data
Selecting a small subset of genes out of the thousands of genes in microarray data is important for accurate classification of phenotypes. Widely used methods typically rank genes according to their differential expressions among phenotypes and pick the top-ranked genes. We observe that feature sets so obtained have certain redundancy and study methods to minimize it. Feature sets obtained through the minimum redundancy - maximum relevance framework represent broader spectrum of characteristics of phenotypes than those obtained through standard ranking methods; they are more robust, generalize well to unseen data, and lead to significantly improved classifications in extensive experiments on 5 gene expressions data sets.
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.
1876538
PSoL: a positive sample only learning algorithm for finding non-coding RNA genes
MOTIVATION
Small non-coding RNA (ncRNA) genes play important regulatory roles in a variety of cellular processes. However, detection of ncRNA genes is a great challenge to both experimental and computational approaches. In this study, we describe a new approach called positive sample only learning (PSoL) to predict ncRNA genes in the Escherichia coli genome. Although PSoL is a machine learning method for classification, it requires no negative training data, which, in general, is hard to define properly and affects the performance of machine learning dramatically. In addition, using the support vector machine (SVM) as the core learning algorithm, PSoL can integrate many different kinds of information to improve the accuracy of prediction. Besides the application of PSoL for predicting ncRNAs, PSoL is applicable to many other bioinformatics problems as well.


RESULTS
The PSoL method is assessed by 5-fold cross-validation experiments which show that PSoL can achieve about 80% accuracy in recovery of known ncRNAs. We compared PSoL predictions with five previously published results. The PSoL method has the highest percentage of predictions overlapping with those from other methods.
53222834
A ghost cell expansion method for reducing communications in solving PDE problems