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.
28835634
A Distance-Based Algorithm for Clustering Database User Sessions
In this paper, we present a distance-based clustering algorithm for grouping database user sessions. The algorithm considers both local and global similarities between sessions and incorporates three distance metrics in the computation of the distance between two sessions. We describe the three metrics and discuss the rational for combining them. The algorithm is evaluated on two datasets. One is a clinic OLTP workload file and the other is the TPC-W benchmark. The evaluation results are reported.
43417272
A platform for Okapi-based contextual information retrieval
We present an extensible java-based platform for contextual retrieval based on the probabilistic information retrieval model. Modules for dual indexes, relevance feedback with blind or machine learning approaches and query expansion with context are integrated into the Okapi system to deal with the contextual information. This platform allows easy extension to include other types of contextual information.
2071657
Comparison of interestingness functions for learning web usage patterns
Livelink is a collaborative intranet, extranet and e-business application that enables employees and business partners of an organization to capture, share and reuse business information and knowledge. The usage of the Livelink software has been recorded by the Livelink Web server in its log files. We present an application of data mining techniques to the Livelink Web usage data. In particular, we focus on how to find interesting association rules and sequential patterns from the Livelink log files. A number of interestingness measures are used in our application to identify interesting rules and patterns. We present a comparison of these measures based on the feedback from domain experts. Some of the interestingness measures are found to be better than others.
16726691
Objective and subjective algorithms for grouping association rules
We propose two algorithms for grouping and summarizing association rules. The first algorithm recursively groups rules according to the structure of the rules and generates a tree of clusters as a result. The second algorithm groups the rules according to the semantic distance between the rules by making use of an automatically tagged semantic tree-structured network of items. We provide a case study in which the proposed algorithms are evaluated. The results show that our grouping methods are effective and produce good grouping results.
10722999
A Case Study for Learning from Imbalanced Data Sets
We present our experience in applying a rule induction technique to an extremely imbalanced pharmaceutical data set. We focus on using a variety of performance measures to evaluate a number of rule quality measures. We also investigate whether simply changing the distribution skew in the training data can improve predictive performance. Finally, we propose a method for adjusting the learning algorithm for learning in an extremely imbalanced environment. Our experimental results show that this adjustment improves predictive performance for rule quality formulas in which rule coverage makes positive contributions to the rule quality value.
6189075
NodeRank: A New Structure Based Approach to Information Filtering
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.
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.
17889266
Negative-Weight Cycle Algorithms
The problem of finding a negative cycle in a weighted, directed graph is discussed here. First the algorithm for printing out a negative cycle reachable from the sources, with the running time no worse than the Bellman-Ford algorithm for the single-source shortest-path problem, is presented. Then an approach with the same time complexity, which could be used for outputting a negative cycle that may not be reachable froms, is reported.
43055665
Automatic Abstraction of Graphs Based on Node Similarity for Graph Visualization