Handling Local Patterns in Collaborative Structuring

Handling Local Patterns in Collaborative Structuring

Ingo Mierswa (University of Dortmund, Germany), Katharina Morik (University of Dortmund, Germany) and Michael Wurst (University of Dortmund, Germany)
Copyright: © 2008 |Pages: 20
DOI: 10.4018/978-1-59904-645-7.ch008
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Media collections in the internet have become a commercial success and the structuring of large media collections has thus become an issue. Personal media collections are locally structured in very different ways by different users. The level of detail, the chosen categories, and the extensions can differ completely from user to user. Can machine learning be of help also for structuring personal collections? Since users do not want to have their hand-made structures overwritten, one could deny the benefit of automatic structuring. We argue that what seems to exclude machine learning, actually poses a new learning task. We propose a notation which allows us to describe machine learning tasks in a uniform manner. Keeping the demands of structuring private collections in mind, we define the new learning task of localized alternative cluster ensembles. An algorithm solving the new task is presented together with its application to distributed media management.

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