Data mining and knowledge discovery can today be considered as stable fields with numerous efficient methods and studies that have been proposed to extract knowledge from data. Nevertheless, the famous golden nugget is still challenging. Actually, the context evolved since the first definition of the KDD process and knowledge has now to be extracted from data getting more and more complex. The structure of the data, for instance, doesn’t match the attribute-value format when considering the web, texts or videos.

In the framework of Data Mining, many software solutions have been developed for the extraction of knowledge from tabular data (which are typically obtained from relational databases). Methodological extensions have been proposed to deal with data initially obtained from other sources, like in the context of natural language (text mining) and image (image mining). KDD has thus evolved following a unimodal scheme instantiated according to the type of the underlying data (tabular data, text, images, etc), which, at the end, always leads to working on the classical double entry tabular format.

The aim of this book is to gather the most recent works that address issues related to the concept of mining complex data. The whole knowledge discovery process being involved, our goal is to provide researchers dealing with each step of this process by key entries. Actually, managing complex data within the KDD process implies to work on every step, starting from the pre-processing (e.g. structuring and organizing) to the visualization and interpretation (e.g. sorting or filtering) of the results, via the data mining methods themselves (e.g. classification, clustering, frequent patterns extraction, etc.). The papers presented here are selected from the workshop papers held yearly since 2006.