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.
14449557
Evaluation and automatic selection of methods for handling missing data
Real-world data often contain missing values. In this paper, nine different methods for handling missing data are described and compared. Thirty real-world datasets are used as input data in our experiment. Nine different missing levels are implemented in each training data set to investigate the performance of the methods at different missing levels. For evaluation, two different classification techniques, C4.5 and ELEM2, are applied to obtain the classification error rates. The experimental results show that different methods can have different effects on a data set. Based on the experimental results, we also propose a meta-learning approach to selecting a method for handling missing data. The evaluation results show that the meta-learning approach is effective in selecting a suitable method for handling missing data.
2832131
Applying Data Mining to Pseudo-Relevance Feedback for High Performance Text Retrieval
In this paper, we investigate the use of data mining, in particular the text classification and co-training techniques, to identify more relevant passages based on a small set of labeled passages obtained from the blind feedback of a retrieval system. The data mining results are used to expand query terms and to re-estimate some of the parameters used in a probabilistic weighting function. We evaluate the data mining based feedback method on the TREC HARD data set. The results show that data mining can be successfully applied to improve the text retrieval performance. We report our experimental findings in detail.
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.
12321641
Locally Balanced Incremental Hierarchical Discriminant Regression
Incremental hierarchical discriminant regression faces several challenging issues: (a) a large input space with a small output space and (b) nonstationary statistics of data sequences. In the first case (a), there maybe few distinct labels in the output space while the input data distribute in a high dimensional space. In the second case (b), a tree has to be grown when only a limited data sequence has been observed. In this paper, we present the Locally Balanced Incremental Hierarchical Discriminant Regression (LBIHDR) algorithm. A novel node self-organization and spawning strategy is proposed to generate a more discriminant subspace by forming multiple clusters for one class. The algorithm was successfully applied to different kinds of data set.
10668543
Parameterized Computation of LCS for Two Sequences
The problem of finding the longest common subsequence is a well-known optimization problem because of its applications, especially in bioinformatics. In this paper, by applying the techniques developed in parameterized computation, an efficient approach for the problem of finding the longest common subsequence of two sequences is presented. The parameterized approach is compared with the well-known dynamic programming approach for the problem. The parameterized approach is much more efficient, as is shown by the experimental results.
5703198
On the Structural Algorithm of Filtering Graphs for Layout
When the amount of information in visualization becomes large enough, users can not perceive all elements at the same time. This problem can be solved by removing parts of the information through the process of Filtering. In this paper, we present a novel method for filtering a graph by measuring the important role property of a node. The basic idea of this approach is to quantify the importance of a node as the degree to which it has direct and indirect relationships with the other nodes in a graph. All the nodes are ranked according to their Node Importance Scores, and those less important nodes and their associated edges are then removed or invisible. In comparison with the rule_based approach, our approach is a structure_based one that makes use of the linkage of nodes rather than their semantics. It can therefore be applied to filter any kinds of connected graphs. The examples and applications provided have demonstrated that our approach can effectively reduce the visual complexities.
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.
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.