Lecture Notes in Computer Science

4 primary works

Data Mining on Multimedia Data

by Petra Perner

Published 13 December 2002

Despite being a young field of research and development, data mining has proved to be a successful approach to extracting knowledge from huge collections of structured digital data collection as usually stored in databases. Whereas data mining was done in early days primarily on numerical data, nowadays multimedia and Internet applications drive the need to develop data mining methods and techniques that can work on all kinds of data such as documents, images, and signals.

This book introduces the basic concepts of mining multimedia data and demonstrates how to apply these methods in various application fields. It is written for students, ambitioned professionals from industry and medicine, and for scientists who want to contribute R&D work to the field or apply this new technology.

Advances in Data Mining

by Petra Perner

Published 9 December 2004

The Industrial Conference on Data Mining ICDM-Leipzig was the fourth meeting in a series of annual events which started in 2000, organized by the Institute of Computer Vision and Applied Computer Sciences (IBaI) in Leipzig. The mission of the conference is to bring together researchers and people from industry in order to discuss together new trends and applications in data mining. This year a broad spectrum of work of different applications was presented ranging from image mining, medicine and biotechnology, management and environmental control, to telecommunications. Besides that an industrial exhibition showed the successful application of data mining methods by industries in different areas such as medical devices, mass data management systems, data mining tools, etc. During the discussion many projects were inspired leading to new and joint work. The fruitful discussions, the exchange of ideas and the spirit of the conference made it a remarkable event for both sides, industry and research. We would like to express our appreciation to the reviewers for their precise and highly professional work. We appreciate the help and understanding of the editorial staff at Springer and in particular Alfred Hofmann, who supported the publication of these proceedings in the LNAI series. Last, but not least, we wish to thank all speakers, participants and industrial exhibitors who contributed to the success of the conference. We are looking forward to welcoming you to ICDM 2005 (www.data-mini- forum.de) and to the new work you will present there.

Advances in Data Mining. Applications and Theoretical Aspects

by Petra Perner

Published 1 January 2011

Machine Learning and Data Mining in Pattern Recognition

by Petra Perner

Published December 2003

We met again in front of the statue of Gottfried Wilhelm von Leibniz in the city of Leipzig. Leibniz, a famous son of Leipzig, planned automatic logical inference using symbolic computation, aimed to collate all human knowledge. Today, artificial intelligence deals with large amounts of data and knowledge and finds new information using machine learning and data mining. Machine learning and data mining are irreplaceable subjects and tools for the theory of pattern recognition and in applications of pattern recognition such as bioinformatics and data retrieval. This was the fourth edition of MLDM in Pattern Recognition which is the main event of Technical Committee 17 of the International Association for Pattern Recognition; it started out as a workshop and continued as a conference in 2003. Today, there are many international meetings which are titled "machine learning" and "data mining", whose topics are text mining, knowledge discovery, and applications. This meeting from the first focused on aspects of machine learning and data mining in pattern recognition problems. We planned to reorganize classical and well-established pattern recognition paradigms from the viewpoints of machine learning and data mining. Though it was a challenging program in the late 1990s, the idea has inspired new starting points in pattern recognition and effects in other areas such as cognitive computer vision.