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Chapter
Improvement on ARC-BC Algorithm in Text Classification Method
With the rapid development of automatic text clustering and classification, many techniques and algorithms studying have been made focused in the field of text categorization. However, there is still much work...
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Chapter and Conference Paper
A SVM Method for Web Page Categorization Based on Weight Adjustment and Boosting Mechanism
Web page classification is an important research direction of web mining. In the paper, a SVM method of web page classification is presented. It include four steps: (1) using analysis module to extract the cor...
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Chapter and Conference Paper
A New Algorithm for Computing the Minimal Enclosing Sphere in Feature Space
The problem of computing the minimal enclosing sphere (MES) of a set of points in the high dimensional kernel-induced feature space is considered. In this paper we develop an entropy-based algorithm that is su...
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Chapter and Conference Paper
Distribution Discovery: Local Analysis of Temporal Rules
In recent years, there has been increased interest in using data mining techniques to extract temporal rules from temporal sequences. Local temporal rules, which only a subsequence exhibits, are actually very ...
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Chapter and Conference Paper
Discovering Local Patterns from Multiple Temporal Sequences
In this paper, we address a data-mining problem that is the discovery of local sequential patterns from a set of long sequences. Each local sequential pattern is represented by a pattern A→B and a time period in ...
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Chapter and Conference Paper
Indexing and Mining of the Local Patterns in Sequence Database
Previous studies on frequent pattern discovery from temporal sequence mainly consider finding global patterns, where every record in a sequence contributes to support the patterns. In this paper, we present a ...
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Chapter and Conference Paper
A Method to Boost Naïve Bayesian Classifiers
In this paper, we introduce a new method to improve the performance of combining boosting and naïve Bayesian. Instead of combining boosting and Naïve Bayesian learning directly, which was proved to be unsatisf...
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Chapter and Conference Paper
A Method to Boost Support Vector Machines
Combining boosting and Support Vector Machine (SVM) is proved to be beneficial, but it is too complex to be feasible. This paper introduces an efficient way to boost SVM. It embraces the idea of active learnin...
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Chapter and Conference Paper
Micro Similarity Queries in Time Series Database
Currently there is no model available that would facilitate the task of finding similar time series based on partial information that interest users. We studied a novel query problem class that we termed micro...
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Chapter and Conference Paper
Concept Approximation in Concept Lattice
In this paper we present a novel approach to the concept approximations in concept lattice. Using the similar idea of rough set theory and unique properties of concept lattice, upper and lower approximations o...
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Chapter and Conference Paper
Learning Bayesian Networks with Hidden Variables Using the Combination of EM and Evolutionary Algorithms
In this paper, a new method, called EM-EA, is put forward for learning Bayesian network structures from incomplete data. This method combines the EM algorithm with an evolutionary algorithm (EA) and transforms...
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Chapter and Conference Paper
A Heuristic Optimal Reduct Algorithm
Reduct finding, especially optimal reduct finding, similar to feature selection problem, is a crucial task in rough set applications to data mining, In this paper, we propose a heuristic reduct finding algorit...
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Chapter and Conference Paper
Incremental Discovering Association Rules: A Concept Lattice Approach
Concept lattice is an efficient tool for data analysis. Mining association rules is a important subfield of data mining. In this paper we investigate the ability of concept lattice on associate rules and prese...
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Chapter and Conference Paper
Integrating Classification and Association Rule Mining: A Concept Lattice Framework
Concept lattice is an efficient tool for data analysis. In this paper we show how classification and association rule mining can be unified under concept lattice framework. We present a fast algorithm to extra...
