One of the superb characteristics of Intelligent Data Analysis (IDA) is that it is an interdisciplinary ?eld in which researchers and practitioners from a number of areas are involved in a typical project. This also creates a challenge in which the success of a team depends on the participation of users and domain experts who need to interact with researchers and developers of any IDA system. All this is usually re?ected in successful projects and of course on the papers that were evaluated by this year’s program committee from which the ?nal program has been developed. In our call for papers, we solicited papers on (i) applications and tools, (ii) theory and general principles, and (iii) algorithms and techniques. We received a total of 184 papers, reviewing these was a major challenge. Each paper was assigned to three reviewers. In the end 46 papers were accepted, which are all included in the proceedings and presented at the conference. This year’s papers re?ect the results of applied and theoretical researchfrom a number of disciplines all of which are related to the ?eld of Intelligent Data Analysis. To have the best combination of theoretical and applied research and also provide the best focus, we have divided this year’s IDA program into tu- rials, invited talks, panel discussions and technical sessions.

Table of Contents:
Probabilistic Latent Clustering of Device Usage.- Condensed Nearest Neighbor Data Domain Description.- Balancing Strategies and Class Overlapping.- Modeling Conditional Distributions of Continuous Variables in Bayesian Networks.- Kernel K-Means for Categorical Data.- Using Genetic Algorithms to Improve Accuracy of Economical Indexes Prediction.- A Distance-Based Method for Preference Information Retrieval in Paired Comparisons.- Knowledge Discovery in the Identification of Differentially Expressed Genes in Tumoricidal Macrophage.- Searching for Meaningful Feature Interactions with Backward-Chaining Rule Induction.- Exploring Hierarchical Rule Systems in Parallel Coordinates.- Bayesian Networks Learning for Gene Expression Datasets.- Pulse: Mining Customer Opinions from Free Text.- Keystroke Analysis of Different Languages: A Case Study.- Combining Bayesian Networks with Higher-Order Data Representations.- Removing Statistical Biases in Unsupervised Sequence Learning.- Learning from Ambiguously Labeled Examples.- Learning Label Preferences: Ranking Error Versus Position Error.- FCLib: A Library for Building Data Analysis and Data Discovery Tools.- A Knowledge-Based Model for Analyzing GSM Network Performance.- Sentiment Classification Using Information Extraction Technique.- Extending the SOM Algorithm to Visualize Word Relationships.- Towards Automatic and Optimal Filtering Levels for Feature Selection in Text Categorization.- Block Clustering of Contingency Table and Mixture Model.- Adaptive Classifier Combination for Visual Information Processing Using Data Context-Awareness.- Self-poised Ensemble Learning.- Discriminative Remote Homology Detection Using Maximal Unique Sequence Matches.- From Local Pattern Mining to Relevant Bi-cluster Characterization.- Machine-Learning with Cellular Automata.- MDS polar : A New Approach for Dimension Reduction to Visualize High Dimensional Data.- Miner Ants Colony: A New Approach to Solve a Mine Planning Problem.- Extending the GA-EDA Hybrid Algorithm to Study Diversification and Intensification in GAs and EDAs.- Spatial Approach to Pose Variations in Face Verification.- Analysis of Feature Rankings for Classification.- A Mixture Model-Based On-line CEM Algorithm.- Reliable Hierarchical Clustering with the Self-organizing Map.- Statistical Recognition of Noun Phrases in Unrestricted Text.- Successive Restrictions Algorithm in Bayesian Networks.- Modelling the Relationship Between Streamflow and Electrical Conductivity in Hollin Creek, Southeastern Australia.- Biological Cluster Validity Indices Based on the Gene Ontology.- An Evaluation of Filter and Wrapper Methods for Feature Selection in Categorical Clustering.- Dealing with Data Corruption in Remote Sensing.- Regularized Least-Squares for Parse Ranking.- Bayesian Network Classifiers for Time-Series Microarray Data.- Feature Discovery in Classification Problems.- A New Hybrid NM Method and Particle Swarm Algorithm for Multimodal Function Optimization.- Detecting Groups of Anomalously Similar Objects in Large Data Sets.