The general theme of the Intelligent Data Analysis (IDA) Symposia is the - telligent use of computers in complex data analysis problems. The ?eld has matured su?ciently that some re-considerationof our objectives was required in order to retain the distinctiveness of IDA. Thus, in addition to the more tra- tional algorithm- and application-oriented submissions, we sought submissions that speci?cally focus on aspects of the data analysis process. For example, - teractive tools to guide and support data analysis in complex scenarios. With the increasingavailabilityofautomaticallycollecteddata,toolsthatintelligently support and assist human analysts are becoming important. IDA-09, the 8th International Symposium on Intelligent Data Analysis, took place in Lyon from August 31 to September 2, 2009. The invited speakers were PaulCohen(UniversityofArizona,USA)andPabloJensen(ENSLyon,France). The meeting received more than 80 submissions. The Programme Committee selected 33 submissions for publication: 18 for full oral presentation, and 15 for poster and short oralpresentation. Eachcontribution was evaluated by three expertsandhas beenallocated12pagesintheproceedings.Theacceptedpapers cover a broad range of topics and applications, and include contributions on the re?
ned focus of IDA.
Reihe
Auflage
Sprache
Verlagsort
Verlagsgruppe
Zielgruppe
Für höhere Schule und Studium
Für Beruf und Forschung
Research
Illustrationen
Maße
Höhe: 235 mm
Breite: 155 mm
Dicke: 24 mm
Gewicht
ISBN-13
978-3-642-03914-0 (9783642039140)
DOI
10.1007/978-3-642-03915-7
Schweitzer Klassifikation
Invited Papers.- Intelligent Data Analysis in the 21st Century.- Analyzing the Localization of Retail Stores with Complex Systems Tools.- Selected Contributions 1 (Long Talks).- Change (Detection) You Can Believe in: Finding Distributional Shifts in Data Streams.- Exploiting Data Missingness in Bayesian Network Modeling.- DEMScale: Large Scale MDS Accounting for a Ridge Operator and Demographic Variables.- How to Control Clustering Results? Flexible Clustering Aggregation.- Compensation of Translational Displacement in Time Series Clustering Using Cross Correlation.- Context-Based Distance Learning for Categorical Data Clustering.- Semi-supervised Text Classification Using RBF Networks.- Improving k-NN for Human Cancer Classification Using the Gene Expression Profiles.- Subgroup Discovery for Test Selection: A Novel Approach and Its Application to Breast Cancer Diagnosis.- Trajectory Voting and Classification Based on Spatiotemporal Similarity in Moving Object Databases.- Leveraging Call Center Logs for Customer Behavior Prediction.- Condensed Representation of Sequential Patterns According to Frequency-Based Measures.- ART-Based Neural Networks for Multi-label Classification.- Two-Way Grouping by One-Way Topic Models.- Selecting and Weighting Data for Building Consensus Gene Regulatory Networks.- Incremental Bayesian Network Learning for Scalable Feature Selection.- Feature Extraction and Selection from Vibration Measurements for Structural Health Monitoring.- Zero-Inflated Boosted Ensembles for Rare Event Counts.- Selected Contributions 2 (Short Talks).- Mining the Temporal Dimension of the Information Propagation.- Adaptive Learning from Evolving Data Streams.- An Application of Intelligent Data Analysis Techniques to a Large Software Engineering Dataset.- Which Distance for the Identification and the Differentiation of Cell-Cycle Expressed Genes?.- Ontology-Driven KDD Process Composition.- Mining Frequent Gradual Itemsets from Large Databases.- Selecting Computer Architectures by Means of Control-Flow-Graph Mining.- Visualization-Driven Structural and Statistical Analysis of Turbulent Flows.- Distributed Algorithm for Computing Formal Concepts Using Map-Reduce Framework.- Multi-Optimisation Consensus Clustering.- Improving Time Series Forecasting by Discovering Frequent Episodes in Sequences.- Measure of Similarity and Compactness in Competitive Space.- Bayesian Solutions to the Label Switching Problem.- Efficient Vertical Mining of Frequent Closures and Generators.- Isotonic Classification Trees.
