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.
6731955
Ubiquitous media agents: a framework for managing personally accumulated multimedia files
Abstract.A novel idea and framework of ubiquitous media agents is presented for managing personal multimedia objects. Media agents are intelligent systems that can autonomously do the following tasks. They automatically collect and build personalized semantic indices of multimedia data on behalf of the user whenever and wherever he accesses/uses these multimedia data. The sources of these semantic descriptions are the textual context of the same documents that contain these multimedia data. The URLs of these multimedia data are indexed using these textual features. When the user wants to use these multimedia data again, the media agents can help the user find relevant multimedia data and provide proper suggestions based on the semantic indices. The media agents learn from the user's interaction records to refine the semantic indices and to model user intentions and preferences. Various algorithms can be used to implement the framework, and a few of them are described in this paper. As shown in the experiments, the media agents are effective in gathering relevant semantics for media objects and learning to provide precise suggestions when the user wants to reuse relevant media objects again.
14132390
Log mining to improve the performance of site search
Despite of the popularity of global search engines,people still suffer from low accuracy of site search. Theprimary reason lies in the difference of link structuresand data scale between global Web and website, whichleads to failures of traditional re-ranking methods suchas HITS, PageRank and DirectHit. This paper proposes anovel re-ranking method based on user logs withinwebsites. With the help of website taxonomy, we mine forgeneralized association rules and abstract accesspatterns of different levels. Mining results aresubsequently used to re-rank the retrieved pages. One ofthe advantages of our mining algorithm is that it resolvesthe diversity problem of user's access behavior anddiscovers general patterns. Experiment shows that theproposed method outperforms keyword-based method by15% and DirectHit by 13% respectively.
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.
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.
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
62617805
Improved rate control for MPEG-4 video transport over wireless channel
Video streaming over wireless channel is challenging due to a number of factors such as limited bandwidth and loss sensitivity. In this paper, we develop a novel rate control algorithm for MPEG-4 video coding. Unlike traditional rate control schemes, we jointly consider the encoding complexity variation and buffer variation as well as human visual properties to optimize the rate control efficiency. We also analyze the sensitivity of a macroblock (MB) as a result of bit errors and calculate its error sensitivity metric. This metric is used in unequal error protection (UEP) of the MB. Simulation results show that proposed approach can improve the decoded picture quality in wireless video coding and transmission.
1822288
Incremental model building of polyhedral objects using structured light
Abstract A vision method using active sensing to construct the complete geometric model of a polyhedral object is presented. The model is constructed incrementally from multiple views. Each view produces a partial object model. Three different methods are described for integrating all the partial models into a final complete model. Theorems regarding the sufficient number of views required and how these views can be set up during the reconstruction process are also given. From theoretical analysis and experimental results, our method is shown to produce an accurate object geometric model, not just an approximate model. Furthermore, it does not rely on a precise object-to-image space transformation. Moreover, the method is fast and robust, since the basic geometric entity it derives is a face instead of a point.
20004613
Disk Embeddings of Planar Graphs
Abstract 
Given a planar graph $G=(V,E)$ and a rooted forest ${\FF}=(V_{\FF}, A_{\FF})$ 
with leaf set $V$, we wish to decide whether $G$ has a plane embedding $\GG$ 
satisfying the following condition: There are $|V_{\FF}|-|V|$ pairwise noncrossing 
Jordan curves in the plane one-to-one corresponding to the nonleaf vertices of 
${\FF}$ such that for every nonleaf vertex $f$ of ${\FF}$, the interior of the curve 
$\JJ_f$ corresponding to $f$ contains all the leaf descendants of $f$ in ${\FF}$
but contains no other leaves of ${\FF}$. 
This problem arises from theoretical studies in geographic database systems. 
It is unknown whether this problem can be solved in polynomial time. 
This paper presents an almost linear-time algorithm for a nontrivial special case 
where the set of leaf descendants of each nonleaf vertex $f$ in ${\FF}$ induces 
a connected subgraph of $G$. 


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.