16574049
Feature Selection with Rough Sets for Web Page Classification
Web page classification is the problem of assigning predefined categories to web pages. A challenge in web page classification is how to deal with the high dimensionality of the feature space. We present a feature reduction method based on the rough set theory and investigate the effectiveness of the rough set feature selection method on web page classification. Our experiments indicate that rough set feature selection can improve the predictive performance when the original feature set for representing web pages is large.
668000
Applying language modeling to session identification from database trace logs
A database session is a sequence of requests presented to the database system by a user or an application to achieve a certain task. Session identification is an important step in discovering useful patterns from database trace logs. The discovered patterns can be used to improve the performance of database systems by prefetching predicted queries, rewriting the current query or conducting effective cache replacement.In this paper, we present an application of a new session identification method based on statistical language modeling to database trace logs. Several problems of the language modeling based method are revealed in the application, which include how to select values for the parameters of the language model, how to evaluate the accuracy of the session identification result and how to learn a language model without well-labeled training data. All of these issues are important in the successful application of the language modeling based method for session identification. We propose solutions to these open issues. In particular, new methods for determining an entropy threshold and the order of the language model are proposed. New performance measures are presented to better evaluate the accuracy of the identified sessions. Furthermore, three types of learning methods, namely, learning from labeled data, learning from semi-labeled data and learning from unlabeled data, are introduced to learn language models from different types of training data. Finally, we report experimental results that show the effectiveness of the language model based method for identifying sessions from the trace logs of an OLTP database application and the TPC-C Benchmark.
9414412
Concept-based biomedical text retrieval
One challenging problem for biomedical text retrieval is to find accurate synonyms or name variants for biomedical entities. In this paper, we propose a new concept-based approach to tackle this problem. In this approach, a set of concepts instead of keywords will be extracted from a query first. Then these concepts will be used for retrieval purpose. The experiment results show that the proposed approach can boost the retrieval performance and it generates very good results on 2005 TREC Genomics data sets.
27390518
Session Boundary Detection for Association Rule Learning Using n-Gram Language Models
We present a statistical method using n-gram language models to identify session boundaries in a large collection of Livelink log data. The identified sessions are then used for association rule learning. Unlike the traditional ad hoc timeout method, which uses fixed time thresholds for session identification, our method uses an information theoretic approach that provides a natural technique for performing dynamic session identification. The effectiveness of our approach is evaluated with respect to 4 different interestingness measures. We find that we obtain a significant improvement in each interestingness measure, ranging from a 26.6% to 39% improvement on average over the best results obtained with standard timeout methods.
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.
9602783
Discovery of interesting association rules from Livelink web log data
We present our experience in mining web usage patterns from a large collection of Livelink log data. Livelink is a web-based product of Open Text, which provides automatic management and retrieval of different types of information objects over an intranet or extranet. We report our experience in preprocessing raw log data and post-processing the mining results for finding interesting rules. In particular we compare and evaluate a number of rule interestingness measures and find that two of the measures that have not been used in association rule learning work very well.
15814097
Language Independent Text Categorization
Text categorization is defined as the task of assigning pre-defined category labels to new documents. This paper proposes a language-independent text categorization model based on machine learning, and describes the feature extraction, classifier and evaluation method in detail. This model has been implemented on two news corpus of Chinese and Japanese and satisfactory categorization effectiveness has been achieved.
44298104
Tight Lower Bounds for Certain Parameterized NP-Hard Problems
6321735
In-Route Skyline Querying for Location-Based Services
With the emergence of an infrastructure for location-aware mobile services, the processing of advanced, location-based queries that are expected to underlie such services is gaining in relevance. While much work has assumed that users move in Euclidean space, this paper assumes that movement is constrained to a road network and that points of interest can be reached via the network. More specifically, the paper assumes that the queries are issued by users moving along routes towards destinations. The paper defines in-route nearest-neighbor skyline queries in this setting and considers their efficient computation. The queries take into account several spatial preferences, and they intuitively return a set of most interesting results for each result returned by the corresponding non-skyline queries. The paper also covers a performance study of the proposed techniques based on real point-of-interest and road network data.
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.