Mining complex dynamic data
author: Arthur Zimek, LMU Institut für Informatik, Ludwig-Maximilians Universität
author: Myra Spiliopoulou, Faculty of Computer Science (FIN), University of Magdeburg
author: Irene Ntoutsi, LMU Institut für Informatik, Ludwig-Maximilians Universität
published: Oct. 3, 2011, recorded: September 2011, views: 486
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In recent years, many applications require mining from richer data types than conventional data(base) records: the analysis of social networks requires the combination of activity recordings with content (e.g. resource descriptions and user records); recommendation engines require considering user ratings, customer transactions, item descriptions and user profiles; medical applications require the combination of different kinds of recordings on patients, including historical data on ailments and medication. At the same time, the mining tasks become more elaborate: the data are multi-faceted and adhere to many, orthogonal or overlapping concepts; the data accumulate or form streams; they are dynamic and call for adaptation of the mining models. In this tutorial, we discuss mining on complex data, putting the emphasis on learning and adaptation over streaming, dynamic data.
We consider three categories of complex data: data that adhere to multiple overlapping labels, high-dimensional data that contain interesting subspaces, and data that span across multiple tables. For each category, we first provide a comprehensive overview of static mining methods, and then focus on methods and example applications for dynamic data. For multi-label stream data, we focus on the example application of document (news) categorization; the core methods are stream classification with decision trees, prediction and ranking. For high-dimensional stream data, we focus on the example application of bioinformatics and network intrusion; the core methods are stream subspace clustering and outlier detection. For multi-relational stream data, we consider two example applications: analysis of dynamic social networks, and analysis of evolving customer data; the core methods are tensor-based clustering, and multi-relational clustering and classification.
The target groups are: postgraduate students with solid background in data mining; research scholars who work on conventional stream mining and are confronted with applications on complex data; practitioners that own applications on complex and dynamic data.
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