Advanced Data Mining and Applications

The Fourth International Conference on Advanced Data Mining and Applications (ADMA 2008) will be held in Chengdu, China, followed by the last three successful ADMA conferences (2005 in Wu Han, 2006 in Xi'an, and 2007 Harbin). Our major goal of ADMA is to bring together the experts on data mining in the world, and to provide a leading international forum for the dissemination of original research results in data mining, including applications, algorithms, software and systems, and different disciplines with potential applications of data mining. This goal 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. ADMA is ranked higher than, or very similar to, other data mining conferences (such as PAKDD, PKDD, and SDM) in early 2008 by an independent source: cs-conference-ranking. org. This year we had the pleasure and honor to host illustrious keynote speakers. Our distinguished keynote speakers are Prof. Qiang Yang and Prof. Jiming Liu. Prof. Yang is a tenured Professor and postgraduate studies coordinator at Computer Science and Engineering Department of Hong Kong University of Science and Technology. He is also a member of AAAI, ACM, a senior member of the IEEE, and he is also an as- ciate editor for the IEEE TKDE and IEEE Intelligent Systems, KAIS and WI Journals.

Klappentext

This book constitutes the refereed proceedings of the 4th International Conference on Advanced Data Mining and Applications, ADMA 2008, held in Chengdu, China, in October 2008.

The 35 revised full papers and 43 revised short papers presented together with the abstract of 2 keynote lectures were carefully reviewed and selected from 304 submissions. The papers focus on advancements in data mining and peculiarities and challenges of real world applications using data mining and feature original research results in data mining, spanning applications, algorithms, software and systems, and different applied disciplines with potential in data mining.

Inhalt
Keynotes.- An Introduction to Transfer Learning.- Autonomy-Oriented Computing (AOC), Self-organized Computability, and Complex Data Mining.- Regular Papers.- Improving Angle Based Mappings.- Mining Natural Language Programming Directives with Class-Oriented Bayesian Networks.- Boosting over Groups and Its Application to Acronym-Expansion Extraction.- A Genetic-Based Feature Construction Method for Data Summarisation.- Suicidal Risk Evaluation Using a Similarity-Based Classifier.- Gene Selection for Cancer Classification Using DCA.- FARS: A Multi-relational Feature and Relation Selection Approach for Efficient Classification.- Enhancing Text Categorization Using Sentence Semantics.- Mining Evolving Web Sessions and Clustering Dynamic Web Documents for Similarity-Aware Web Content Management.- Data Quality in Privacy Preservation for Associative Classification.- Timeline Analysis of Web News Events.- Analysis of Alarm Sequences in a Chemical Plant.- Speed Up SVM Algorithm for Massive Classification Tasks.- Mining Supplemental Frequent Patterns.- A Distributed Privacy-Preserving Association Rules Mining Scheme Using Frequent-Pattern Tree.- Dichotomy Method toward Interactive Testing-Based Fault Localization.- Maintaining the Maximum Normalized Mean and Applications in Data Stream Mining.- Identification of Interface Residues Involved in Protein-Protein Interactions Using Naïve Bayes Classifier.- Negative Generator Border for Effective Pattern Maintenance.- CommTracker: A Core-Based Algorithm of Tracking Community Evolution.- Face Recognition Using Clustering Based Optimal Linear Discriminant Analysis.- A Novel Immune Based Approach for Detection of Windows PE Virus.- Using Genetic Algorithms for Parameter Optimization in Building Predictive Data Mining Models.- Using DataMining Methods to Predict Personally Identifiable Information in Emails.- Iterative Reinforcement Cross-Domain Text Classification.- Extracting Decision Rules from Sigmoid Kernel.- DMGrid: A Data Mining System Based on Grid Computing.- S-SimRank: Combining Content and Link Information to Cluster Papers Effectively and Efficiently.- Open Domain Recommendation: Social Networks and Collaborative Filtering.- An Effective Approach for Identifying Evolving Three-Dimensional Structural Motifs in Protein Folding Data.- Texture Image Retrieval Based on Contourlet Transform and Active Perceptual Similarity Learning.- A Temporal Dominant Relationship Analysis Method.- Leakage-Aware Energy Efficient Scheduling for Fixed-Priority Tasks with Preemption Thresholds.- Short Papers.- Learning and Inferences of the Bayesian Network with Maximum Likelihood Parameters.- TARtool: A Temporal Dataset Generator for Market Basket Analysis.- Dimensionality Reduction for Classification.- Trajectories Mining for Traffic Condition Renewing.- Mining Bug Classifier and Debug Strategy Association Rules for Web-Based Applications.- Test the Overall Significance of p-values by Using Joint Tail Probability of Ordered p-values as Test Statistic.- Mining Interesting Infrequent and Frequent Itemsets Based on MLMS Model.- Text Learning and Hierarchical Feature Selection in Webpage Classification.- The RSO Algorithm for Reducing Number of Set Operations in Association Rule Mining.- Predictive Performance of Clustered Feature-Weighting Case-Based Reasoning.- Selecting the Right Features for Bipartite-Based Text Clustering.- Image Emotional Classification Based on Color Semantic Description.- A Semi-supervised Clustering Algorithm Based on Must-Link Set.- T-rotation: Multiple Publications of Privacy PreservingData Sequence.- The Integrated Methodology of KPCA and Wavelet Support Vector Machine for Predicting Financial Distress.- Outlier Detection Based on Voronoi Diagram.- AWSum Data Mining for Insight.- Integrative Neural Network Approach for Protein Interaction Prediction from Heterogeneous Data.- Rules Extraction Based on Data Summarisation Approach Using DARA.- A Rough-Apriori Technique in Mining Linguistic Association Rules.- Mining Causal Knowledge from Diagnostic Knowledge.- Modified Particle Swarm Optimizer with Adaptive Dynamic Weights for Cancer Combinational Chemotherapy.- MPSQAR: Mining Quantitative Association Rules Preserving Semantics.- Using Support Vector Regression for Classification.- Dynamic Growing Self-organizing Neural Network for Clustering.- A Design of Reward Function Based on Knowledge in Multi-agent Learning.- A Learning Method of Detecting Anomalous Pedestrian.- Moment+: Mining Closed Frequent Itemsets over Data Stream.- CDPM: Finding and Evaluating Community Structure in Social Networks.- Using Matrix Model to Find Association Rule Core for Diverse Compound Critiques.- Link-Contexts for Ranking.- DC-Tree: An Algorithm for Skyline Query on Data Streams.- Sequential Pattern Mining for Protein Function Prediction.- Improving Web Search by Categorization, Clustering, and Personalization.- JSNVA: A Java Straight-Line Drawing Framework for Network Visual Analysis.- Recognition of Data Records in Semi-structured Web-Pages Using Ontology and ? 2 Statistical Distribution.- Organizing Structured Deep Web by Clustering Query Interfaces Link Graph.- CBP: A New Efficient Method for Mining Multilevel and Generalized Frequent Itemsets.- Supporting Customer Retention through Real-Time Monitoring of Individual Web Usage.- A Comparative Study of CorrelationMeasurements for Searching Similar Tags.- Structure of Query Modification Process: Branchings.- Mining Top-n Local Outliers in Constrained Spatial Networks.- Mining Concept-Drifting Data Streams with Multiple Semi-Random Decision Trees.- Automatic Web Tagging and Person Tagging Using Language Models.- Real-Time Person Tracking Based on Data Field.