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.
17711119
IRC: an iterative reinforcement categorization algorithm for interrelated Web objects
Most existing categorization algorithms deal with homogeneous Web data objects, and consider interrelated objects as additional features when taking the interrelationships with other types of objects into account. However, focusing on any single aspects of these interrelationships and objects does not fully reveal their true categories. In this paper, we propose a categorization algorithm, the iterative reinforcement categorization algorithm (IRC), to exploit the full interrelationships between the heterogeneous objects on the Web. IRC attempts to classify the interrelated Web objects by iterative reinforcement between individual classification results of different types via the interrelationships. Experiments on a clickthrough log dataset from MSN search engine show that, with the Fl measures, IRC achieves a 26.4% improvement over a pure content-based classification method, a 21% improvement over a query metadata-based method, and a 16.4% improvement over a virtual document-based method. Furthermore, our experiments show that IRC converges rapidly.
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.
459986
A New Statistical Approach To Chinese Pinyin Input
Chinese input is one of the key challenges for Chinese PC users. This paper proposes a statistical approach to Pinyin-based Chinese input. This approach uses a trigram-based language model and a statistically based segmentation. Also, to deal with real input, it also includes a typing model which enables spelling correction in sentence-based Pinyin input, and a spelling model for English which enables modeless Pinyin input.
432736
ReCoM: reinforcement clustering of multi-type interrelated data objects
Most existing clustering algorithms cluster highly related data objects such as Web pages and Web users separately. The interrelation among different types of data objects is either not considered, or represented by a static feature space and treated in the same ways as other attributes of the objects. In this paper, we propose a novel clustering approach for clustering multi-type interrelated data objects, ReCoM (Reinforcement Clustering of Multi-type Interrelated data objects). Under this approach, relationships among data objects are used to improve the cluster quality of interrelated data objects through an iterative reinforcement clustering process. At the same time, the link structure derived from relationships of the interrelated data objects is used to differentiate the importance of objects and the learned importance is also used in the clustering process to further improve the clustering results. Experimental results show that the proposed approach not only effectively overcomes the problem of data sparseness caused by the high dimensional relationship space but also significantly improves the clustering accuracy.
14828051
MRSSA: an iterative algorithm for similarity spreading over interrelated objects
We introduce the Multiple Relationship Similarity Spreading Algorithm (MRSSA) to enhance IR effectiveness. This method has similarity computed in an iterative "spreading" fashion for multiple object types, combining both inter- and intra-object relationships. We demonstrate the value of this approach in the context of the WWW, where the key objects are web pages and queries, Relationships considered are derived from hyperlinks (in- and out-links) and click-through logs.
17921413
A self-learning worm using importance scanning
The use of side information by an attacker can help a worm speed up the propagation. This philosophy has been the basis for advanced worm scanning mechanisms such as hitlist scanning, routable scanning, and importance scanning. Some of these scanning methods use information on vulnerable hosts. Such information, however, may not be easy to collect before a worm is released. Questions then arise whether and how a worm can self-learn and use such information while propagating, and how virulent the resulting worm may be. In this paper, we design a self-learning worm using importance scanning. An optimal yet practical importance-scanning strategy is derived based on a new metric. A self-learning worm is demonstrated to have the ability to accurately estimate the underlying vulnerable-host distribution if a sufficient number of infected hosts are observed. Experimental results based on parameters chosen from Code Red show that after accurately estimating the distribution of vulnerable hosts, a self-learning worm can spread much faster than a random-scanning worm, a permutation-scanning worm, and a Class A routing worm. Some guidelines for detecting and defending against such self learning worms are also discussed.
8030523
Video management in peer-to-peer systems
Providing scalable video services in a peer-to-peer (P2P) environment is challenging. Since videos are typically large and require high communication bandwidth for delivery, many peers may be unwilling to cache them in whole to serve others. In this paper, the authors addressed two fundamental research problems in providing scalable P2P video services, namely (1) how a host can find enough video pieces, which may scatter among the whole system, to assemble a complete video, and (2) given a limited buffer size, what part of a video a host should cache. A new distributed video management technique was proposed. This scheme organizes hosts into a number of cells, each of which is a distinct set of hosts which together can supply a video in its entirety. A client looking for a video can stop its search as soon as it finds a host that caches any part of the video. Caching operations can be coordinated within each cell to balance data redundancy in the system. The extensive study on a Gnutella-like simulation network shows convincingly the performance advantage of the new scheme.
1853937
Numerically Stable Real Number Codes Based on Random Matrices
Error correction codes defined over real-number field have been studied and recognized as useful in many applications. However, most real-number codes in literature are quite suspect in their numerical stability. In this paper, we introduce a class of real-number codes based on random generator matrices over real-number fields. Codes over complex-number field are also discussed. Experiment results demonstrate our codes are numerically much more stable than existing codes in literature.
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.
37360166
PAWS: Personalized Adaptive Web Search