18862916
Clustering graphs for visualization via node similarities
Graph visualization is commonly used to visually model relations in many areas. Examples include Web sites, CASE tools, and knowledge representation. When the amount of information in these graphs becomes too large, users, however, cannot perceive all elements at the same time. A clustered graph can greatly reduce visual complexity by temporarily replacing a set of nodes in clusters with abstract nodes. This paper proposes a new approach to clustering graphs. The approach constructs the node similarity matrix of a graph that is derived from a novel metric of node similarity. The linkage pattern of the graph is thus encoded into the similarity matrix, and then one obtains the hierarchical abstraction of densely linked subgraphs by applying the k-means algorithm to the matrix. A heuristic method is developed to overcome the inherent drawbacks of the k-means algorithm. For clustered graphs we present a multilevel multi-window approach to hierarchically drawing them in different abstract level views with the purpose of improving their readability. The proposed approaches demonstrate good results in our experiments. As application examples, visualization of part of Java class diagrams and Web graphs are provided. We also conducted usability experiments on our algorithm and approach. The results have shown that the hierarchically clustered graph used in our system can improve user performance for certain types of tasks.
8009887
A Framework of Filtering, Clustering and Dynamic Layout Graphs for Visualization
Many classical graph visualization algorithms have already been developed over the past decades. However, these algorithms face difficulties in practice, such as the overlapping node problem, large graph layout and dynamic graph layout. In order to solve these problems, this paper aims to systematically address algorithmic issues related to a novel framework that describes the process of graph visualization applications. First of all, a framework for graph visualization is described. As the important parts of this framework, we then develop two effective algorithms for filtering and clustering large graphs for the layouts. As for the dynamic graph layout, a new approach to removing overlapping nodes called force-transfer algorithm is developed. The framework has been implemented in a prototype called PGA to demonstrate the performance of the proposed algorithms. Finally, a case study is provided.
16444798
Finding Similar Patterns in Microarray Data
In this paper we propose a clustering algorithm called s-Cluster for analysis of gene expression data based on pattern-similarity. The algorithm captures the tight clusters exhibiting strong similar expression patterns in Microarray data,and allows a high level of overlap among discovered clusters without completely grouping all genes like other algorithms. This reflects the biological fact that not all functions are turned on in an experiment, and that many genes are co-expressed in multiple groups in response to different stimuli. The experiments have demonstrated that the proposed algorithm successfully groups the genes with strong similar expression patterns and that the found clusters are interpretable.
5775998
Identification of clusters in the Web graph based on link topology
The Web graph has recently been used to model the link structure of the Web. The studies of such graphs can yield valuable insights into Web algorithms for crawling, searching and discovery of Web communities. This paper proposes a new approach to clustering the Web graph. The proposed algorithm identifies a small subset of the graph as "core" members of clusters, and then incrementally constructs the clusters by a selection criterion. Two qualitative criteria are proposed to measure the quality of graph clustering. We have implemented our algorithm and tested a set of arbitrary graphs with good results. Applications of our approach include graph drawing and Web visualization.
17691685
WFMS-based Data Integration for e-Learning
As more and more organisations and institutions are moving towards the e-learning strategy, more and more disparate data are distributed by different e-learning systems. How to effectively use this vast amount of distributed data becomes a big challenge. This paper addresses this challenge and works out a new mechanism to implement data integration for e-learning. A workflow management system based (WFMS-based) data integration model is contributed to the e-learning
6189075
NodeRank: A New Structure Based Approach to Information Filtering
8542665
Polynomial time approximation schemes and parameterized complexity
In this paper, we study the relationship between the approximability and the parameterized complexity of NP optimization problems. We introduce a notion of polynomial fixed-parameter tractability and prove that, under a very general constraint, an NP optimization problem has a fully polynomial time approximation scheme if and only if the problem is polynomial fixed-parameter tractable. By enforcing a constraint of planarity on the W-hierarchy studied in parameterized complexity theory, we obtain a class of NP optimization problems, the planar W-hierarchy, and prove that all problems in this class have efficient polynomial time approximation schemes (EPTAS). The planar W-hierarchy seems to contain most of the known EPTAS problems, and is significantly different from the class introduced by Khanna and Motwani in their efforts in characterizing optimization problems with polynomial time approximation schemes.
921552
Dynamic Web log session identification with statistical language models
We present a novel session identification method based on statistical language modeling. Unlike standard time-out methods, which use fixed time thresholds for session identification, we use an information theoretic approach that yields more robust results for identifying session boundaries. We evaluate our new approach by learning interesting association rules from the segmented session files. We then compare the performance of our approach to three standard session identification method--the standard timeout method, the reference length method, and the maximal forward reference method--and find that our statistical language modeling approach generally yields superior results. However, as with every method, the performance of our technique varies with changing parameter settings. Therefore, we also analyze the influence of the two key factors in our language-modeling-based approach: the choice of smoothing technique and the language model order. We find that all standard smoothing techniques, save one, perform well, and that performance is robust to language model order.
1605324
Using contextual information to improve retrieval performance
In this paper, we propose a contextual retrieval framework which incorporates the user and global contextual information into the probabilistic retrieval model. We investigate different techniques of using contextual information to improve information retrieval performance in details. In particular, (1) we use the related text contextual information for query expansion; (2) we use the granularity information to construct the document level index and paragraph level index; (3) we use the geographic information for filtering. In addition, a new term weighting function BM5O is proposed based on the global context information. This framework is adaptable and extensible. If there is a new context category, we can extend the existing search system to accommodate it. Finally, we report our experimental findings on TREC data sets.
7754676
FDUQA on TREC2003 QA task
It is the fourth time that we take part in the QA track. Our system, FDUQA, is based on our previous system (Wu et al, 2002). FDUQA includes an offline part and an online part. We make great efforts on the online part while leaving the offline part unchanged. We have tried many natural language processing techniques, and incorporated many sources of world knowledge, including Web. A novel Query formulation technique has also been put forward. In addition, we’ve tried another attempt on answer extraction in this year’s task. In the second section, we will describe the architecture of our QA system; and give a detailed description on the Query formulation for Web search in the third section; while in the fourth section, we will introduce our new attempt on answer extraction; and we will present our performance in the last section.
28347745
On global sequence alignment
We present a dynamic programming algorithm for computing a best global alignment of two sequences. The proposed algorithm is robust in identifying any of several global relationships between two sequences. The algorithm delivers a best alignment of two sequences in linear space and quadratic time. We also describe a multiple alignment algorithm based on the pairwise algorithm. Both algorithms have been implemented as portable C programs. Experimental results indicate that for a commonly used set of gap penalties, the new programs produce more satisfactory alignments on sequences of various lengths than some existing pairwise and multiple programs based on the dynamic programming algorithm of Needleman and Wunsch.