11783493
Unsupervised Feature Selection Via Two-way Ordering in Gene Expression Analysis
MOTIVATION
Selection of genes most relevant and informative for certain phenotypes is an important aspect in gene expression analysis. Most current methods select genes based on known phenotype information. However, certain set of genes may correspond to new phenotypes which are yet unknown, and it is important to develop novel effective selection methods for their discovery without using any prior phenotype information.


RESULTS
We propose and study a new method to select relevant genes based on their similarity information only. The method relies on a mechanism for discarding irrelevant genes. A two-way ordering of gene expression data can force irrelevant genes towards the middle in the ordering and thus can be discarded. Mechanisms based on variance and principal component analysis are also studied. When applied to expression profiles of colon cancer and leukemia, the unsupervised method outperforms the baseline algorithm that simply uses all genes, and it also selects relevant genes close to those selected using supervised methods.


SUPPLEMENT
More results and software are online: http://www.nersc.gov/~cding/2way.
30201789
Term norm distribution and its effects on Latent Semantic Indexing
Latent Semantic Indexing (LSI) uses the singular value decomposition to reduce noisy dimensions and improve the performance of text retrieval systems. Preliminary results have shown modest improvements in retrieval accuracy and recall, but these have mainly explored small collections. In this paper we investigate text retrieval on a larger document collection (TREC) and focus on distribution of word norm (magnitude). Our results indicate the inadequacy of word representations in LSI space on large collections. We emphasize the query expansion interpretation of LSI and propose an LSI term normalization that achieves better performance on larger collections.
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.
45840953
Dynamic Cluster Formation Using Level Set Methods
Density-based clustering has the advantages for (i) allowing arbitrary shape of cluster and (ii) not requiring the number of clusters as input. However, when clusters touch each other, both the cluster centers and cluster boundaries (as the peaks and valleys of the density distribution) become fuzzy and difficult to determine. In higher dimension, the boundaries become wiggly and over-fitting often occurs. We introduce the notion of cluster intensity function (CIF) which captures the important characteristics of clusters. When clusters are well-separated, CIFs are similar to density functions. But as clusters touch each other, CIFs still clearly reveal cluster centers, cluster boundaries, and, degree of membership of each data point to the cluster that it belongs. Clustering through bump hunting and valley seeking based on these functions are more robust than that based on kernel density functions which are often oscillatory or over-smoothed. These problems of kernel density estimation are resolved using Level Set Methods and related techniques. Comparisons with two existing density-based methods, valley seeking and DBSCAN, are presented to illustrate the advantages of our approach.
807817
A spectral method to separate disconnected and nearly-disconnected web graph components
Separation of connected components from a graph with disconnected graph components mostly use breadth-first search (BFS) or depth-first search (DFS) graph algorithms. Here we propose a new algebraic method to separate disconnected and nearly-disconnected components. This method is based on spectral graph partitioning, following a key observation that disconnected components will show up, after properly sorted, as step-function like curve in the lowest eigenvectors of the Laplacian matrix of the graph. Following an perturbative analysis framework, we systematically analyzed the graph structures, first on the disconnected subgraph case, and second on the effects of adding edges sparsely connecting different subgraphs as a perturbation. Several new results are derived, providing insights to spectral methods and related clustering objective function. Examples are given illustrating the concepts and results our methods. Comparing to the standard graph algorithms, this method has the same O(‖E ‖ + ‖V‖log(‖V‖)) complexity, but is easier to implement (using readily available eigensolvers). Further more the method can easily identify articulation points and bridges on nearly-disconnected graphs. Segmentation of a real example of Web graph for query amazon is given. We found that each disconnected or nearly-disconnected components forms a cluster on a clear topic.
13840611
Dynamic cluster formation using level set methods
Density-based clustering has the advantages for: 1) allowing arbitrary shape of cluster and 2) not requiring the number of clusters as input. However, when clusters touch each other, both the cluster centers and cluster boundaries (as the peaks and valleys of the density distribution) become fuzzy and difficult to determine. We introduce the notion of cluster intensity function (CIF) which captures the important characteristics of clusters. When clusters are well-separated, CIFs are similar to density functions. But, when clusters become closed to each other, CIFs still clearly reveal cluster centers, cluster boundaries, and degree of membership of each data point to the cluster that it belongs. Clustering through bump hunting and valley seeking based on these functions are more robust than that based on density functions obtained by kernel density estimation, which are often oscillatory or oversmoothed. These problems of kernel density estimation are resolved using level set methods and related techniques. Comparisons with two existing density-based methods, valley seeking and DBSCAN, are presented which illustrate the advantages of our approach.
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.
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.
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.
36544447
What Are People Looking for in Your Web Page?
Web server log analyses usually analyze the pattern of the access. We believe that it is also very important to understand the goal of the access. In this paper, we propose to combine the log analysis with content analysis to identify information goals on individual accessed pages. We analyze the web server log to extract information goals on entry pages from anchor texts and query terms, and propagate them along users’ access paths to other linked pages. The experiment shows that our approach could find popular terms on web pages, temporal changes in these terms could reflect users’ interest shifts, and unexpected terms could sometimes indicate a design problem.
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.