Discovering Multiple Clustering Solutions: Grouping Objects in Different Views of the Data
Traditional clustering algorithms identify just a single clustering of the data. Today's complex data, however, allows multiple interpretations leading to several valid groupings hidden in different views of the database. Each of these multiple clustering solutions is valuable and interesting as different perspectives on the same data and several meaningful groupings for each object are given. Especially for high dimensional data where each object is described by multiple attributes, alternative clusters in different attribute subsets are of major interest.
In this tutorial, we describe several real world application scenarios for multiple clustering solutions. We abstract from these scenarios and provide the general challenges in this emerging research area. We describe state-of-the-art paradigms, we highlight specific techniques, and we give an overview of this topic by providing a taxonomy of the existing methods. By focusing on open challenges, we try to attract young researchers for participating in this emerging research field.
Our target is a broad audience ranging from researches focusing on formal methods up to application oriented practitioners from industry. Researches from the area of clustering but also from related directions might contribute to this field. For all of these people, multiple clustering solutions will be of great interest.
|Authors:||Müller E., Günnemann S., Färber I., Seidl T.|
|Published in:||Tutorial at IEEE International Conference on Data Mining (ICDM 2010), Sydney, Australia|
|Publisher:||IEEE Computer Society - Washington,USA|
|Research topic:||Data Analysis and Knowledge Extraction|