MHDW 2012 - 1st Mining Humanistic Data Workshop (MHDW 2012)

Workshop Aim: The abundance of available data that is retrieved from or is related to the areas of Humanities challenges the research community in processing and analyzing it. The aim is two-fold: on the one hand, to extract knowledge that will help understand human behavior, creativity, way of thinking, reasoning, learning, decision making, socializing; on the other hand, to exploit the extracted knowledge by incorporating it into intelligent systems that will support humans in their everyday activities.
The nature of humanistic data can be multimodal, dynamic, time and space-dependent, and highly complicated. Translating humanistic information, e.g. behavior, state of mind, artistic creation and linguistic utterance, into numerical or categorical low-level data is a significant challenge on its own. New mining techniques, appropriate to deal with this type of data, need to be proposed and existing ones adapted to its special characteristics.
The workshop aims to bring together interdisciplinary approaches that focus on the application of innovative as well as existing mining and knowledge discovery techniques (like decision rules, decision trees, association rules, clustering, filtering, learning, classifier systems, neural networks, support vector machines, preprocessing, post processing, feature selection, visualization techniques) to data derived from all areas of Humanistic Sciences, e.g. linguistic, historical, behavioral, psychological, artistic, musical, educational, social etc.
Workshop Topics: The workshop topics include but are not limited to:
Humanistic Data Collection and Interpretation
Data pre-processing
Feature Selection
Supervised learning of humanistic knowledge
Clustering
Knowledge Representation and Reasoning
Linguistic Data Mining
Historical Research
Educational Data Mining
Music Information Retrieval
Data-driven Profiling/ Personalization
User Modeling
Behavior Prediction
Recommender Systems
Web Sentiment Analysis
Social Data Mining
Visualization techniques
Integration of data mining results into real-world applications with humanistic context