8890999
A unified framework for clustering heterogeneous Web objects
We introduce a novel framework for clustering Web data which is often heterogeneous in nature. As most existing methods often integrate heterogeneous data into a unified feature space, their flexibilities to explore and adjust contributing effects from different heterogeneous information are compromised. In contrast, our framework enables separate clustering of homogeneous data in the entire process based on their respective features, and a layered structure with link information is used to iteratively project and propagate the clustered results between layers until it converges. Our experimental results show that such a scheme not only effectively overcomes the problem of data sparseness caused by the high dimensional link space but also improves the clustering accuracy significantly. We achieve 19% and 41% performance increases when clustering Web-pages and users based on a semi-synthetic Web log. Finally, we show a real clustering result based on UC Berkeley's Web log.
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.
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.
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.
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.
3449616
Fast and memory-efficient regular expression matching for deep packet inspection
Packet content scanning at high speed has become extremely important due to its applications in network security, network monitoring, HTTP load balancing, etc. In content scanning, the packet payload is compared against a set of patterns specified as regular expressions. In this paper, we first show that memory requirements using traditional methods are prohibitively high for many patterns used in packet scanning applications. We then propose regular expression rewrite techniques that can effectively reduce memory usage. Further, we develop a grouping scheme that can strategically compile a set of regular expressions into several engines, resulting in remarkable improvement of regular expression matching speed without much increase in memory usage. We implement a new DFA-based packet scanner using the above techniques. Our experimental results using real-world traffic and patterns show that our implementation achieves a factor of 12 to 42 performance improvement over a commonly used DFA- based scanner. Compared to the state-of-art NFA-based implementation, our DFA-based packet scanner achieves 50 to 700 times speedup.
6847311
On the Complexity of Rocchio's Similarity-Based Relevance Feedback Algorithm
In this paper, we prove for the first time that the learning complexity of Rocchio's algorithm is O(d + d2(log d + log n)) over the discretized vector space {0,...,n–1}d, when the inner product similarity measure is used. The upper bound on the learning complexity for searching for documents represented by a monotone linear classifier (q,0) over {0,...,n–1}d can be improved to O(d + 2k(n–1)(log d + log(n–1))), where k is the number of nonzero components in q. An Ω((d2)log n) lower bound on the learning complexity is also obtained for Rocchio's algorithm over {0,...,n–1}d. In practice, Rocchio's algorithm often uses fixed query updating factors. When this is the case, the lower bound is strengthened to 2$^{{\it \Omega}(d)}$ over the binary vector space {0,1}d. In general, if the query updating factors are bounded by O(nc) for some constant c≥ 0, an ${\it \Omega}(n^{d-1-c}/(n-1))$ lower bound is obtained over {0,...,n–1}d.
46287200
A Parallel Algorithm for Finding a Triconnected Component Separator with an Application
7582784
Hierarchical Clustering-Merging for Multidimensional Index Structures
The R-tree family index structures are among the most common index structures used in multidimensional databases. To improve the search performance it is very important to reduce the overlap between bounding regions in the R-tree. However the arbitrary insertion order in the tree construction procedure might result in tree structures inefficient in the search operations. In this paper we propose a new technique called Hierarchical Clustering-Merging (HCM) to improve the tree construction procedure of the R-tree family index structures. With this technique we can take advantage of the data distribution information in the data set to achieve an optimized tree structure and improve the search performance.
13035041
Searching the Web with Queries
In this paper we study the problem of searching the Web with online learning algorithms. We consider that Web documents can be represented by vectors of n boolean attributes. A search engine is viewed as a learner, and a user is viewed as a teacher. We investigate the number of queries a search engine needs from the user to search for a collection of Web documents. We design several efficient learning algorithms to search for any collection of documents represented by a disjunction (or a conjunction) of relevant attributes with the help of membership queries or equivalence queries.