Advanced Data Mining and Applications

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.

Klappentext
This book constitutes the refereed proceedings of the Third International Conference on Advanced Data Mining and Applications, ADMA 2007, held in Harbin, China in August 2007. The 44 revised full papers and 15 revised short papers presented together with the abstract of 1 invited lecture were carefully reviewed and selected from about 200 submissions. The papers focus on advancements in data mining and peculiarities and challenges of real world applications using data mining. The major theme of the conference encompasses the innovative applications of data mining approaches to real-world problems that involve large data sets, incomplete and noisy data, or demand optimal solutions.

Inhalt
Invited Talk.- Mining Ambiguous Data with Multi-instance Multi-label Representation.- Regular Papers.- 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 onEnhanced Association Rule.- Applications of the Moving Average of n th -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.- Short Papers.- 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.- 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.