Mining Complex Data ECML/PKDD 2007 Third International Workshop, MDC 2007, Warsaw, Poland, September 17-21, 2007, Revised Selected Papers

This book constitutes the refereed proceedings of the Third International Workshop on Mining Complex Data, MCD 2007, held in Warsaw, Poland, in September 2007, co-located with ECML and PKDD 2007. The 20 revised full papers presented were carefully reviewed and selected; they present original results on knowledge discovery from complex data. In contrast to the typical tabular data, complex data can consist of heterogenous data types, can come from different sources, or live in high dimensional spaces. All these specificities call for new data mining strategies.

Session A1.- Using Text Mining and Link Analysis for Software Mining.- Generalization-Based Similarity for Conceptual Clustering.- Trajectory Analysis of Laboratory Tests as Medical Complex Data Mining.- Session A2.- Conceptual Clustering Applied to Ontologies.- Feature Selection: Near Set Approach.- Evaluating Accuracies of a Trading Rule Mining Method Based on Temporal Pattern Extraction.- Session A3.- Discovering Word Meanings Based on Frequent Termsets.- Quality of Musical Instrument Sound Identification for Various Levels of Accompanying Sounds.- Discriminant Feature Analysis for Music Timbre Recognition and Automatic Indexing.- Session A4.- Contextual Adaptive Clustering of Web and Text Documents with Personalization.- Improving Boosting by Exploiting Former Assumptions.- Discovery of Frequent Graph Patterns that Consist of the Vertices with the Complex Structures.- Session B1.- Finding Composite Episodes.- Ordinal Classification with Decision Rules.- Data Mining of Multi-categorized Data.- ARAS: Action Rules Discovery Based on Agglomerative Strategy.- Session B2.- Learning to Order: A Relational Approach.- Using Semantic Distance in a Content-Based Heterogeneous Information Retrieval System.- Using Secondary Knowledge to Support Decision Tree Classification of Retrospective Clinical Data.- POM Centric Multi-aspect Data Analysis for Investigating Human Problem Solving Function.