MultiClust 2013 - 4th Workshop on Multiple Clusterings, Multi-View Data, and Multi-Source Knowledge-Driven Clustering, in Conj. with the 19th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, KDD 2013

Cluster detection is a very traditional data analysis task with several decades of research. However, it also includes a large variety of different subtopics investigated by different communities such as data mining, machine learning, statistics, and database systems. "Multiple Clusterings, Multi-view Data, and Multi-source Knowledge-driven Clustering" names several challenges around clustering: making sense or even making use of many, possibly redundant clustering results, of different representations and properties of data, of different sources of knowledge. Approaches such as ensemble clustering, semi-supervised clustering, subspace clustering meet around these problems. Yet they tackle these problems with different backgrounds, focus on different details, and include ideas from different research communities. This diversity is a major potential for this emerging field and should be highlighted by this workshop. A core motivation for this workshop series is our believe that these approaches are not just tackling different parts of the problem but that they should benefit from each other and, ultimately, combine the different perspectives and techniques to tackle the clustering problem more effectively. In paper presentations and discussions, we therefore would like to encourage the workshop participants to look at their own research problems from multiple perspectives.