Advanced data mining and applications : Third International Conference, ADMA 2007 Harbin, China, August 6-8, 2007 : proceedings
The Third International Conference on Advanced Data Mining and Applications (ADMA) organized in Harbin, China continued the tradition already established by the first two ADMA conferences in Wuhan in 2005 and Xi'an in 2006. One major goal of ADMA is to create a respectable identity in the data...
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| Main Authors | , |
|---|---|
| Format | eBook Book |
| Language | English |
| Published |
Berlin
Springer
2007
Springer Berlin / Heidelberg |
| Edition | 1 |
| Series | Lecture Notes in Computer Science |
| Subjects | |
| Online Access | Get full text |
| ISBN | 3540738703 9783540738701 |
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| Abstract | The Third International Conference on Advanced Data Mining and Applications (ADMA) organized in Harbin, China continued the tradition already established by the first two ADMA conferences in Wuhan in 2005 and Xi'an in 2006. One major goal of ADMA is to create a respectable identity in the data mining research com- nity. This feat has been partially achieved in a very short time despite the young age of the conference, thanks to the rigorous review process insisted upon, the outstanding list of internationally renowned keynote speakers and the excellent program each year. The impact of a conference is measured by the citations the conference papers receive. Some have used this measure to rank conferences. For example, the independent source cs-conference-ranking.org ranks ADMA (0.65) higher than PAKDD (0.64) and PKDD (0.62) as of June 2007, which are well established conferences in data mining. While the ranking itself is questionable because the exact procedure is not disclosed, it is nevertheless an encouraging indicator of recognition for a very young conference such as ADMA. |
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| AbstractList | The Third International Conference on Advanced Data Mining and Applications (ADMA) organized in Harbin, China continued the tradition already established by the first two ADMA conferences in Wuhan in 2005 and Xi'an in 2006. One major goal of ADMA is to create a respectable identity in the data mining research com- nity. This feat has been partially achieved in a very short time despite the young age of the conference, thanks to the rigorous review process insisted upon, the outstanding list of internationally renowned keynote speakers and the excellent program each year. The impact of a conference is measured by the citations the conference papers receive. Some have used this measure to rank conferences. For example, the independent source cs-conference-ranking.org ranks ADMA (0.65) higher than PAKDD (0.64) and PKDD (0.62) as of June 2007, which are well established conferences in data mining. While the ranking itself is questionable because the exact procedure is not disclosed, it is nevertheless an encouraging indicator of recognition for a very young conference such as ADMA. |
| Author | ADMA Alhajj, Reda |
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| Notes | Includes bibliographical references and index |
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| Snippet | The Third International Conference on Advanced Data Mining and Applications (ADMA) organized in Harbin, China continued the tradition already established by... |
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| SubjectTerms | Cluster analysis Cluster analysis -- Congresses Computer algorithms Computer algorithms -- Congresses Congresses Data mining Data mining -- Congresses |
| TableOfContents | Intro -- Preface -- Organization -- Table of Contents -- Mining Ambiguous Data with Multi-instance Multi-label Representation -- DELAY: A Lazy Approach for Mining Frequent Patterns over High Speed Data Streams -- Exploring Content and Linkage Structures for Searching Relevant Web Pages -- CLBCRA-Approach for Combination of Content-Based and Link-Based Ranking in Web Search -- Rough Sets in Hybrid Soft Computing Systems -- Discovering Novel Multistage Attack Strategies -- Privacy Preserving DBSCAN Algorithm for Clustering -- A New Multi-level Algorithm Based on Particle Swarm Optimization for Bisecting Graph -- A Supervised Subspace Learning Algorithm: Supervised Neighborhood Preserving Embedding -- A k-Anonymity Clustering Method for Effective Data Privacy Preservation -- LSSVM with Fuzzy Pre-processing Model Based Aero Engine Data Mining Technology -- A Coding Hierarchy Computing Based Clustering Algorithm -- Mining Both Positive and Negative Association Rules from Frequent and Infrequent Itemsets -- Survey of Improving Naive Bayes for Classification -- Privacy Preserving BIRCH Algorithm for Clustering over Arbitrarily Partitioned Databases -- Unsupervised Outlier Detection in Sensor Networks Using Aggregation Tree -- Separator: Sifting Hierarchical Heavy Hitters Accurately from Data Streams -- Spatial Fuzzy Clustering Using Varying Coefficients -- Collaborative Target Classification for Image Recognition in Wireless Sensor Networks -- Dimensionality Reduction for Mass Spectrometry Data -- The Study of Dynamic Aggregation of Relational Attributes on Relational Data Mining -- Learning Optimal Kernel from Distance Metric in Twin Kernel Embedding for Dimensionality Reduction and Visualization of Fingerprints -- Efficiently Monitoring Nearest Neighbors to a Moving Object -- A Novel Text Classification Approach Based on Enhanced Association Rule Research on the Traffic Matrix Based on Sampling Model -- A Causal Analysis for the Expenditure Data of Business Travelers -- A Visual and Interactive Data Exploration Method for Large Data Sets and Clustering -- Explorative Data Mining on Stock Data - Experimental Results and Findings -- Graph Structural Mining in Terrorist Networks -- Characterizing Pseudobase and Predicting RNA Secondary Structure with Simple H-Type Pseudoknots Based on Dynamic Programming -- Locally Discriminant Projection with Kernels for Feature Extraction -- A GA-Based Feature Subset Selection and Parameter Optimization of Support Vector Machine for Content - Based Image Retrieval -- E-Stream: Evolution-Based Technique for Stream Clustering -- H-BayesClust: A New Hierarchical Clustering Based on Bayesian Networks -- An Improved AdaBoost Algorithm Based on Adaptive Weight Adjusting -- Author Index Applications of the Moving Average of nth-Order Difference Algorithm for Time Series Prediction -- Inference of Gene Regulatory Network by Bayesian Network Using Metropolis-Hastings Algorithm -- A Consensus Recommender for Web Users -- Constructing Classification Rules Based on SVR and Its Derivative Characteristics -- Hiding Sensitive Associative Classification Rule by Data Reduction -- AOG-ags Algorithms and Applications -- A Framework for Titled Document Categorization with Modified Multinomial Naivebayes Classifier -- Prediction of Protein Subcellular Locations by Combining K-Local Hyperplane Distance Nearest Neighbor -- A Similarity Retrieval Method in Brain Image Sequence Database -- A Criterion for Learning the Data-Dependent Kernel for Classification -- Topic Extraction with AGAPE -- Clustering Massive Text Data Streams by Semantic Smoothing Model -- GraSeq: A Novel Approximate Mining Approach of Sequential Patterns over Data Stream -- A Novel Greedy Bayesian Network Structure Learning Algorithm for Limited Data -- Optimum Neural Network Construction Via Linear Programming Minimum Sphere Set Covering -- How Investigative Data Mining Can Help Intelligence Agencies to Discover Dependence of Nodes in Terrorist Networks -- Prediction of Enzyme Class by Using Reactive Motifs Generated from Binding and Catalytic Sites -- Bayesian Network Structure Ensemble Learning -- Fusion of Palmprint and Iris for Personal Authentication -- Enhanced Graph Based Genealogical Record Linkage -- A Fuzzy Comprehensive Clustering Method -- CACS: A Novel Classification Algorithm Based on Concept Similarity -- Data Mining in Tourism Demand Analysis: A Retrospective Analysis -- Chinese Patent Mining Based on Sememe Statistics and Key-Phrase Extraction -- Classification of Business Travelers Using SVMs Combined with Kernel Principal Component Analysis A Novel Text Classification Approach Based on Enhanced Association Rule -- Introduction -- Related Works -- Problems Description -- Predicate Association Rule -- Association Rule Based Text Classification -- Mining Frequent Item-Set on Item-Weighted Transactions -- Designing PAR Based Text Classifier -- Mining Frequent Item-Sets on Item-Weighted Transactions -- Generate PARs -- PARs Based Text Classification -- Experiments and Analysis -- Dataset and Environment -- Experiment -- Conclusions -- References -- Applications of the Moving Average of nth-Order Difference Algorithm for Time Series Prediction -- Introduction -- Time Series Prediction -- The Approach of Moving Average of n^th -Order Difference for Bounded Time Series Prediction -- Description of Sunspots Data Set -- Prediction Results for the Sunspots Data Case Study -- Conclusions and Future Work -- Inference of Gene Regulatory Network by Bayesian Network Using Metropolis-Hastings Algorithm -- Introduction -- Material -- Methods and Experiment -- Metropolis - Hastings Algorithm -- Bayesian Network -- Learning of Bayesian Network by Metropolis-Hastings Algorithm for Gene Regulatory Network Inference -- Results -- Conclusion -- References -- A Consensus Recommender for Web Users -- Introduction and Related Work -- Hybrid Recommender System Modules -- Click-Stream Tree Module -- Markov Module -- Web Page Prediction Model -- Data Preparation and Cleaning -- Representation of Usage Patterns -- Algorithm for Generating Recommendations -- Performance Evaluation -- Experimental Setup -- Effects of Using Different Recommendation Models -- Effects of the Modules -- Effects of the Methods for Updating the Weights -- Analysis of the Recommendation Set -- Conclusion and Future Work -- Constructing Classification Rules Based on SVR and Its Derivative Characteristics -- Introduction Introduction -- MLMS Model -- Application of Correlation Coefficient in PNARs and the Measure VARCC -- Algorithm Design -- Experimental Results -- Conclusions and Future Work -- References -- Survey of Improving Naive Bayes for Classification -- Introduction -- Improved Naive Bayesian Classifiers -- Improve Naive Bayes Using Feature Selection -- Improve Naive Bayes Using Structure Extension -- Improve Naive Bayes Using Local Learning -- Improve Naive Bayes Using Data Expansion -- Experimental Methodology and Results -- Conclusions and Future Work -- Privacy Preserving BIRCH Algorithm for Clustering over Arbitrarily Partitioned Databases -- Introduction -- Related Work -- Secure BIRCH Algorithm -- Arbitrarily Partitioned Databases -- Problem Statement -- Cluster Features -- Secure Distance Metrics -- Secure Construction of CF Tree -- Proof of Correctness -- Security Analysis -- Complexity Analysis -- Special Cases -- Conclusion and Future Work -- Unsupervised Outlier Detection in Sensor Networks Using Aggregation Tree -- Introduction -- Preliminary -- Unsupervised Outlier Detection Using Aggregation Tree -- Algorithm for Snapshot Query -- Update of Outliers for Continuous Query -- Proof of Correctness -- Experimental Evaluation -- Experimentation Setup -- Impact of Sliding Window Size -- Impact of Number of Reported Outliers -- Summary -- Separator: Sifting Hierarchical Heavy Hitters Accurately from Data Streams -- Introduction -- Problem Specification -- Related Work -- Our Approach -- Data Structure -- Algorithm Separator -- Space Complexity and Error Bound -- Experimental Evaluation -- Accuracy and Resource Requirements -- Scalability -- Conclusions -- References -- Spatial Fuzzy Clustering Using Varying Coefficients -- Introduction -- Background and Related Work -- Problem Formulation -- Related Work -- The NEMV Algorithm Intro -- Title -- Preface -- Organization -- Table of Contents -- Mining Ambiguous Data with Multi-instance Multi-label Representation -- DELAY: A Lazy Approach for Mining Frequent Patterns over High Speed Data Streams -- Introduction -- Background and Related Work -- Problem Definition -- A New Approach -- Data Structures -- Algorithm DELAY -- Properties of DELAY -- Comparison with Existing Work -- Experimental Results -- DELAY -- Comparison with LossyCounting and FDPM -- Conclusions -- Exploring Content and Linkage Structures for Searching Relevant Web Pages -- Introduction -- Background -- New Approaches (MDP and QCS) -- A Prototypical System for Searching Relevant Pages -- Experiments -- Conclusions -- CLBCRA-Approach for Combination of Content-Based and Link-Based Ranking in Web Search -- Introduction -- Related Works -- Pagerank -- HITS -- TFIDF -- Computing the Pagerank -- Precision and Recall -- New Model of CLBCRA -- Transition Probability -- Computing the Eigenvalue -- Experimental -- Experimental Setup -- Constructing the Test Data-Set -- Irreducible and Aperiodic -- Experiment Results -- Conclusion -- Rough Sets in Hybrid Soft Computing Systems -- Introduction -- Rough Sets and Soft Computing -- Hybrid Soft Computing Systems Based on Rough Sets -- Attribute Reduction by Rough Sets -- Measuring Uncertainty in Data by Rough Sets -- Mining Knowledge by Rough Sets -- A General Observation -- Conclusions -- References -- Discovering Novel Multistage Attack Strategies -- Introduction -- Related Work -- Generating Pattern Rules -- Problem Statement -- Preparation for GSP -- Using GSP to Discover Sequential Patterns -- Incremental Mining -- Problem Statement -- Incremental Update -- Discussion -- Improvement -- Directed Graph -- Probabilistic Correlation -- Experiment -- DARPA 2000 Experiment -- Live Network Experiment NEM for Spatial Clustering -- NEM with Varying Coefficients -- Experimental Evaluation -- Performance Criteria -- Experimental Results -- Conclusion -- Collaborative Target Classification for Image Recognition in Wireless Sensor Networks -- Introduction -- The Principle of Incremental SVM -- Basis SVM -- Incremental Training of SVM -- Collaborative SVM Based Target Classification -- The Basis of Target Detection and Feature Extraction -- The Principle of Collaborative SVM -- Experimental Results -- Deployment of Experiment -- Multi-target Classification Results and Performance Comparison -- Conclusions -- References -- Dimensionality Reduction for Mass Spectrometry Data -- Introduction -- Wavelet Analysis -- Support Vector Machine -- Results -- Conclusions -- References -- The Study of Dynamic Aggregation of Relational Attributes on Relational Data Mining -- Introduction -- Aggregation and Dynamic Aggregation of Relational Attributes -- Degrees of Aggregation -- Dynamic Aggregation of Relational Attributes -- Types of Discretization -- Experimental Evaluations -- Mutagenesis Dataset -- Musk -- Financial Dataset -- Conclusions -- References -- Learning Optimal Kernel from Distance Metric in Twin Kernel Embedding for Dimensionality Reduction and Visualization of Fingerprints -- Introduction -- Related Work -- Twin Kernel Embedding -- From Distance to Kernel -- Experimental Results -- Conclusions -- Efficiently Monitoring Nearest Neighbors to a Moving Object -- Introduction -- Preliminaries -- Environment -- Query Definition -- Range Filter-Based Approach (RFA) -- Range Filter -- Finding Nearest Neighbors from Filter Pool -- Algorithm Description -- Experiments -- Handling a Static Query Object -- Analysis on Communication Cost -- The Impact of Tolerance -- Related Work -- Conclusions Representation of the Classification Rules Conclusion and Future Work -- References -- Privacy Preserving DBSCAN Algorithm for Clustering -- Introduction -- Related Work -- Preliminaries -- DBSCAN Algorithm -- Secure DBSCAN Algorithm -- Distributed Data Mining -- Problem Statement -- Protocols -- Proposed Algorithms -- Conclusion and Future Work -- A New Multi-level Algorithm Based on Particle Swarm Optimization for Bisecting Graph -- Introduction -- Mathematical Description -- Motivation -- A New Multi-level Particle Swarm Optimization Refinement Algorithm -- Experimental Results -- Conclusions -- A Supervised Subspace Learning Algorithm: Supervised Neighborhood Preserving Embedding -- Introduction -- Neighborhood Preserving Embedding (NPE) -- Supervised NPE -- Experimental Results on the USPS Database -- Feature Extraction Analysis and Data Visualization -- SNPE for Classification -- Conclusions -- References -- A k-Anonymity Clustering Method for Effective Data Privacy Preservation -- Introduction -- The Proposed k-Anonymity Clustering Method -- A Weighted Feature C-Means Clustering Algorithm -- A Class-Merging Mechanism -- Experiments -- Information Distortion -- Classification Error Rate -- Computational Efficiency -- Conclusion -- References -- LSSVM with Fuzzy Pre-processing Model Based Aero Engine Data Mining Technology -- Introduction -- Model Based Engine Data Mining -- Method -- Model Based Aero Engine Fault Diagnostic -- Case Study -- Conclusion and Future Work -- References -- A Coding Hierarchy Computing Based Clustering Algorithm -- Introduction -- Preliminaries -- CHCC Algorithm -- Distance Computing Algorithm -- Algorithm for Computing Median of Hierarchy Coding Variants -- Algorithm CHCC_MAIN -- Algorithm Analyses -- Experiments and Capability Analysis -- Conclusions -- References -- Mining Both Positive and Negative Association Rules from Frequent and Infrequent Itemsets |
| Title | Advanced data mining and applications : Third International Conference, ADMA 2007 Harbin, China, August 6-8, 2007 : proceedings |
| URI | https://cir.nii.ac.jp/crid/1130282271512292608 https://ebookcentral.proquest.com/lib/[SITE_ID]/detail.action?docID=3063397 https://ebookcentral.proquest.com/lib/[SITE_ID]/detail.action?docID=6413195 https://www.vlebooks.com/vleweb/product/openreader?id=none&isbn=9783540738718 |
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