Emerging Technologies in Knowledge Discovery and Data Mining: PAKDD 2007 International Workshops, Nanjing, China, May 22-25, 2007, Revised Selected Pa [Paperback]

PAKDD Industrial Track and Workshops 2007.- PAKDD 2007 Industrial Track Workshop.- A Survey of Open Source Data Mining Systems.- Predicting the Short-Term Market Reaction to Asset Specific News: Is Time Against Us?.- Frequency-Weighted Fuzzy Time-Series Based on Fibonacci Sequence for TAIEX Forecasting.- Probabilistic Techniques for Corporate Blog Mining.- Mining Chat Conversations for Sex Identification.- Mining High Impact Exceptional Behavior Patterns.- Practical Issues on Privacy-Preserving Health Data Mining.- Data Mining for Intelligent Structure Form Selection Based on Association Rules from a High Rise Case Base.- CommonKADS Methodology for Developing Power Grid Switching Orders Systems.- Discovering Prediction Model for Environmental Distribution Maps.- Workshop BioDM07An Overview.- Extracting Features from Gene Ontology for the Identification of Protein Subcellular Location by Semantic Similarity Measurement.- Detecting Community Structure in Complex Networks by Optimal Rearrangement Clustering.- The HIV Data Mining Tool for Government Decision-Making Support.- Negative Localized Relationship Among p70S6 with Smad1, 2, 3 and p38 in Three Treated Human Cancer Cell Lines.- Cancer Identification Based on DNA Microarray Data.- Incorporating Dictionary Features into Conditional Random Fields for Gene/Protein Named Entity Recognition.- Translation and Rotation Invariant Mining of Frequent Trajectories: Application to Protein Unfolding Pathways.- Genetic-Annealing Algorithm for 3D Off-lattice Protein Folding Model.- Biclustering of Microarray Data Based on Singular Value Decomposition.- On the Number of Partial Least Squares Components in Dimension Reduction for Tumor Classification.- Mining Biosignal Data: Coronary Artery Disease Diagnosis Using Linear andNonlinear Features of HRV.- High Performance Data Mining and Applications Overview.- Approximately Mining Recently Representative Patterns on Data Streams.- Finding Frequent Items in Data Streams Using ESBF.-ls'