647726
Learning unions of two rectangles in the plane with equivalence queries
This paper presents an (J(logz n) on-line learning alg~ rithm for unions of two rectangle in the discrete plane {1,..., n}z with equivalence queries. The hypothesis space of the learning algorithm is the same as the concept class. The learning algorithm consists of separate search procedures that search for the boundaries of the target concept. Compared to learning a single rectangle studied in [CM], learning uniona of two rectangles is substantially more complicated in two aspects: the obvious local search procedures have to tolerate both false positive and false negative counterexamples; it is nontrivial for the global algorithm to manage ita local search procedures. In order to overcome those difficulties, more powerful error-tolerant search strategies and management techniques have been developed in this paper.
44651708
Personalized Web Search with Clusters
28060413
The Architecture of Yarrow: A Real-Time Intelligent Meta-Search Engine
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.
4216648
Optimizing web search using web click-through data
The performance of web search engines may often deteriorate due to the diversity and noisy information contained within web pages. User click-through data can be used to introduce more accurate description (metadata) for web pages, and to improve the search performance. However, noise and incompleteness, sparseness, and the volatility of web pages and queries are three major challenges for research work on user click-through log mining. In this paper, we propose a novel iterative reinforced algorithm to utilize the user click-through data to improve search performance. The algorithm fully explores the interrelations between queries and web pages, and effectively finds "virtual queries" for web pages and overcomes the challenges discussed above. Experiment results on a large set of MSN click-through log data show a significant improvement on search performance over the naive query log mining algorithm as well as the baseline search engine.
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.
5899080
Optimizing Web Search Using Spreading Activation on the Clickthrough Data
In this paper, we propose a mining algorithm to utilize the user click-through data to improve search performance. The algorithm first explores the relationship between queries and Web pages and mine out co-visiting relationship as the virtual link among the Web pages, and then Spreading Activation mechanism is used to perform the query-dependent search. Our approach could overcome the challenges discussed above and the experimental results on a large set of MSN click-through log data show a significant improvement on search performance over the DirectHit algorithm as well as the baseline search engine.
16272457
SimFusion: measuring similarity using unified relationship matrix
In this paper we use a Unified Relationship Matrix (URM) to represent a set of heterogeneous data objects (e.g., web pages, queries) and their interrelationships (e.g., hyperlinks, user click-through sequences). We claim that iterative computations over the URM can help overcome the data sparseness problem and detect latent relationships among heterogeneous data objects, thus, can improve the quality of information applications that require com- bination of information from heterogeneous sources. To support our claim, we present a unified similarity-calculating algorithm, SimFusion. By iteratively computing over the URM, SimFusion can effectively integrate relationships from heterogeneous sources when measuring the similarity of two data objects. Experiments based on a web search engine query log and a web page collection demonstrate that SimFusion can improve similarity measurement of web objects over both traditional content based algorithms and the cutting edge SimRank algorithm.
12341856
Similarity spreading: a unified framework for similarity calculation of interrelated objects
In many Web search applications, similarities between objects of one type (say, queries) can be affected by the similarities between their interrelated objects of another type (say, Web pages), and vice versa. We propose a novel framework called similarity spreading to take account of the interrelationship and improve the similarity calculation. Experiment results show that the proposed framework can significantly improve the accuracy of the similarity measurement of the objects in a search engine.