28060413
The Architecture of Yarrow: A Real-Time Intelligent Meta-Search Engine
14616525
Some Formal Analysis of Rocchio's Similarity-Based Relevance Feedback Algorithm
Rocchio's similarity-based Relevance feedback algorithm, one of the most important query reformation methods in information retrieval, is essentially an adaptive supervised learning algorithm from examples. In spite of its popularity in various applications there is little rigorous analysis of its learning complexity in literature. In this paper we show that in the binary vector space model, if the initial query vector is 0, then for any of the four typical similarities (inner product, dice coefficient, cosine coefficient, and Jaccard coefficient), Rocchio's similarity-based relevance feedback algorithm makes at least n mistakes when used to search for a collection of documents represented by a monotone disjunction of at most k relevant features (or terms) over the n-dimensional binary vector space {0, 1}n. When an arbitrary initial query vector in {0, 1}n is used, it makes at least (n + k − 3)/2 mistakes to search for the same collection of documents. The linear lower bounds are independent of the choices of the threshold and coefficients that the algorithm may use in updating its query vector and making its classification.
5722664
A Visualization System using Data Mining Techniques for Identifying Information Sources
The Visual Analysis System (VAS) was developed to couple emerging successes in data mining with information visualization techniques in order to create a richly interactive environment for information retrieval from the World Wide Web. VAS's retrieval strategy operates by first using a conventional search engine to form a core set,of retrieved documents. This core set is expanded by crawling from links in these documents to form a much larger set. The link topology of the larger set is then examined using data mining techniques to identify pages likely to be both relevant to the query and reliable as sources of information. Information visualization techniques are used to display the filtered set in a form amenable to the use of perceptual processes to filter and guide further user directed search. (Contains 16 references.) (MES) Reproductions supplied by EDRS are the best that can be made from the original document. N tr) 00 7171A Visualization System using Data Mining Techniques for Identifying Information Sources Richard H. Fowler, Tarkan Karadayi, Zhixiang Chen, Xiannong Meng, Wendy A. Lawrence Fowler Department of Computer Science University of Texas Pan American United States {fowler, tkaradayi, chen, meng, wfowler @panam.edu} PERMISSION TO REPRODUCE AND DISSEMINATE THIS MATERIAL HAS BEEN GRANTED BY
140623
Optimal Algorithms for Finding User Access Sessions from Very Large Web Logs
Although efficient identification of user access sessions from very large web logs is an unavoidable data preparation task for the success of higher level web log mining, little attention has been paid to algorithmic study of this problem. In this paper we consider two types of user access sessions, interval sessions and gap sessions. We design two efficient algorithms for finding respectively those two types of sessions with the help of some proposed structures. We present theoretical analysis of the algorithms and prove that both algorithms have optimal time complexity and certain error-tolerant properties as well. We conduct empirical performance analysis of the algorithms with web logs ranging from 100 megabytes to 500 megabytes. The empirical analysis shows that the algorithms just take several seconds more than the baseline time, i.e., the time needed for reading the web log once sequentially from disk to RAM, testing whether each user access record is valid or not, and writing each valid user access record back to disk. The empirical analysis also shows that our algorithms are substantially faster than the sorting based session finding algorithms. Finally, optimal algorithms for finding user access sessions from distributed web logs are also presented.
7399730
An Almost Linear Time 2.8334-Approximation Algorithm for the Disc Covering Problem
The disc covering problem asks to cover a set of points on the plane with a minimum number of fix-sized discs. We develop an O(n(logn)2(loglogn)2) deterministic time 2.8334-approximation algorithm for this problem. Previous approximation algorithms [7,3,6], when used to achieve the same approximation ratio for the disc covering problem, will have much higher time complexity than our algorithms.
14422953
On-line learning of rectangles
This paper solves the following open problem: Is there an algorithmfor on-line learning of rectangles <inline-equation><f><pr align="c"><ll>i=1</ll><ul>d</ul></pr></f><?Pub Caret></inline-equation><?Pub Fmt italic>a<subscrpt>i</subscrpt>,a<subscrpt>i</subscrpt>+1,…,b<subscrpt>i</subscrpt>}<?Pub Fmt /italic>over a discrete domain<?Pub Fmt italic>{1,…,n}<supscrpt>d</supscrpt><?Pub Fmt /italic>whose error bound is polylogarithmic in the size<?Pub Fmt italic>n<supscrpt>d</supscrpt><?Pub Fmt /italic> of the domain(i.e. polynomial in <?Pub Fmt italic>d<?Pub Fmt /italic> and log<?Pub Fmt italic>n<?Pub Fmt /italic> )? We give a positive solution byintroducing a new design technique that appears to be of some intereston its own. The new learning algorithm for rectangles consists of<?Pub Fmt italic>2d<?Pub Fmt /italic> separate search strategies thatsearch for the parameters <?Pub Fmt italic>a<subscrpt>1</subscrpt>,b<subscrpt>1</subscrpt>,…,a<subscrpt>d</subscrpt>,b<subscrpt>d</subscrpt><?Pub Fmt /italic>of the target rectangle. A learning algorithm with this type of modulardesign ends to fail because of the well known “credit assignmentproblem”: Which of the <?Pub Fmt italic>2d<?Pub Fmt /italic> localsearch strategies should be “blamed” when the globalalgorithm makes an error? We overcome this difficulty by employing localsearch strategies (“error tolerant binary search”) that areable to tolerate certain types of wrong credit assignments.
Section 4 contains another application of this design technique: analgorithm for learning the union of two rectangles in the plane.
3177556
A parallel architecture for probabilistic relaxation operations on images
Abstract The design of a parallel architecture for executing probabilistic relaxation operations on two dimensional images is addressed. First of all, the concerned relaxation process is divided into three different parallel operations, i.e. systolic, simultaneous, and pipelined. All these parallel operations are mapped onto a linear array architecture that runs smoothly without any bottleneck in the data flows. The proposed architecture and its components are described. An illustrative running of the relaxation process for an image thresholding application on the architecture is described in some details. The hardware implementation and CAD logic simulation are then given. Finally, performance comparison between the proposed architecture and some existing ones is reported.
9623784
Multi-type Features Based Web Document Clustering
Clustering has been demonstrated as a feasible way to explore the contents of document collection and organize search engine results. For this task, many features of Web page, such as content, anchor text, URL, hyperlink etc, can be exploited and different results can be obtained. We expect to provide a unified and even better result for end users. Some work have studied how to use several types of features together to perform clustering. Most of them focus on ensemble method or combination of similarity. In this paper, we propose a novel algorithm: Multi-type Features based Reinforcement Clustering (MFRC). This algorithm does not use a unique combine score for all feature spaces, but uses the intermediate clustering result in one feature space as additional information to gradually enhance clustering in other spaces. Finally a consensus can be achieved by such mutual reinforcement. And the experimental results show that MFRC also provides some performance improvement.
6802371
Improving web search results using affinity graph
In this paper, we propose a novel ranking scheme named Affinity Ranking (AR) to re-rank search results by optimizing two metrics: (1) diversity -- which indicates the variance of topics in a group of documents; (2) information richness -- which measures the coverage of a single document to its topic. Both of the two metrics are calculated from a directed link graph named Affinity Graph (AG). AG models the structure of a group of documents based on the asymmetric content similarities between each pair of documents. Experimental results in Yahoo! Directory, ODP Data, and Newsgroup data demonstrate that our proposed ranking algorithm significantly improves the search performance. Specifically, the algorithm achieves 31% improvement in diversity and 12% improvement in information richness relatively within the top 10 search results.
52859343
Towards rate-distortion tradeoff in real-time color video coding
In this paper, we address to the key problem in real-time video coding, the rate-distortion tradeoff. We found that the distribution of the integer transform coefficients in H.264 color video coding can be approximated by Laplacian distribution but the rate-distortion characteristics should be modelled for the luminance and chrominance components separately. So we propose separable rate-distortion models for color video coding. The feedback from the encoder buffer is analyzed by a control-theoretic adaptation approach. To achieve smooth video quality and satisfy the delay constraints in real-time applications, a novel rate distortion tradeoff controller is employed. Simulations results demonstrate that the proposed approach can achieve smooth video quality without sacrificing overall quality while efficiently utilize the bandwidth without buffer overflow or underflow.
1830855
Web mining for Web image retrieval
The popularity of digital images is rapidly increasing due to improving digital imaging technologies and convenient availability facilitated by the Internet. However, how to find user-intended images from the Internet is non-trivial. The main reason is that the web images are usually not annotated using semantic descriptors. In this paper, we present an effective approach to and a prototype system for image retrieval from the Internet using web mining. The system can also serve as a web image search engine. One of the key ideas in the approach is to extract the text information on the web pages to semantically describe the images. The text description is then combined with other low-level image features in the image similarity assessment. Another main contribution of this work is that we apply data mining on the log of user’s feedback to improve image retrieval performance in three aspects: First, the accuracy of the document space model of image representation obtained from the web pages is improved by removing clutter and irrelevant text information; Second, to construct the user space model of users’ representation of images, which is then combined with the document space model to eliminate mismatch between the page author’s expression and the user’s understanding and expectation; Third, to discover the relationship between low-level and high-level features, which is extremely useful for assigning the low-level features’ weights in similarity assessment.