15395970
Towards an adaptive and task-specific ranking mechanism in Web searching (poster session)
A baler has a preservative applicator assembly with an improved nozzle array for applying a volatile crop treating fluid to crop material as the crop material is being formed into a bale. The nozzle has a centrally disposed passageway which has a cross-sectional area greater than the minimum sum of the cross-sectional areas of a plurality of fluid dispensing openings in the end of the nozzle. Each of the openings includes a throat and a lip outwardly diverging from the throat. The nozzle array maintains the fluid under high pressure and aids in dispersing the fluid with a high velocity to enhance the retention of the fluid as a liquid while in the nozzle and to promote wide and uniform dispersing of the fluid throughout the crop material during the application.
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.
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.
209398610
Beyond the traditional query operators.
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.
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.
1241483
Linearized cluster assignment via spectral ordering
Spectral clustering uses eigenvectors of the Laplacian of the similarity matrix. They are most conveniently applied to 2-way clustering problems. When applying to multi-way clustering, either the 2-way spectral clustering is recursively applied or an embedding to spectral space is done and some other methods are used to cluster the points. Here we propose and study a K-way cluster assignment method. The method transforms the problem to find valleys and peaks of a 1-D quantity called cluster crossing, which measures the symmetric cluster overlap across a cut point along a linear ordering of the data points. The method can either determine K clusters in one shot or recursively split a current cluster into several smaller ones. We show that a linear ordering based on a distance sensitive objective has a continuous solution which is the eigenvector of the Laplacian, showing the close relationship between clustering and ordering. The method relies on the connectivity matrix constructed as the truncated spectral expansion of the similarity matrix, useful for revealing cluster structure. The method is applied to newsgroups to illustrate introduced concepts; experiments show it outperforms the recursive 2-way clustering and the standard K-means clustering.
5650583
Bipartite graph partitioning and data clustering
Many data types arising from data mining applications can be modeled as bipartite graphs, examples include terms and documents in a text corpus, customers and purchasing items in market basket analysis and reviewers and movies in a movie recommender system. In this paper, we propose a new data clustering method based on partitioning the underlying bipartite graph. The partition is constructed by minimizing a normalized sum of edge weights between unmatched pairs of vertices of the bipartite graph. We show that an approximate solution to the minimization problem can be obtained by computing a partial singular value decomposition (SVD) of the associated edge weight matrix of the bipartite graph. We point out the connection of our clustering algorithm to correspondence analysis used in multivariate analysis. We also briefly discuss the issue of assigning data objects to multiple clusters. In the experimental results, we apply our clustering algorithm to the problem of document clustering to illustrate its effectiveness and efficiency.
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.
15511813
Adaptive dimension reduction for clustering high dimensional data
It is well-known that for high dimensional data clustering, standard algorithms such as EM and K-means are often trapped in a local minimum. Many initialization methods have been proposed to tackle this problem, with only limited success. In this paper we propose a new approach to resolve this problem by repeated dimension reductions such that K-means or EM are performed only in very low dimensions. Cluster membership is utilized as a bridge between the reduced dimensional subspace and the original space, providing flexibility and ease of implementation. Clustering analysis performed on highly overlapped Gaussians, DNA gene expression profiles and Internet newsgroups demonstrate the effectiveness of the proposed algorithm.
45336398
Cluster Aggregate Inequality and Multi-level Hierarchical Clustering
We show that (1) in hierarchical clustering, many linkage functions satisfy a cluster aggregate inequality, which allows an exact O(N2) multi-level (using mutual nearest neighbor) implementation of the standard O(N3) agglomerative hierarchical clustering algorithm. (2) a desirable close friends cohesion of clusters can be translated into kNN consistency which is guaranteed by the multi-level algorithm; (3) For similarity-based linkage functions, the multi-level algorithm is naturally implemented as graph contraction. The effectiveness of our algorithms is demonstrated on a number of real life applications.