Integrative Parameter-Free Clustering of Data with Mixed Type Attributes
| Autor: | Bianca Wackersreuther, Michael Plavinski, Sebastian Goebl, Annahita Oswald, Claudia Plant, Christian Bohm |
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| Rok vydání: | 2010 |
| Předmět: |
Clustering high-dimensional data
Fuzzy clustering Computer science business.industry Correlation clustering Constrained clustering computer.software_genre Machine learning Data set Data stream clustering CURE data clustering algorithm Canopy clustering algorithm Data mining Artificial intelligence Cluster analysis business computer Categorical variable |
| Zdroj: | Advances in Knowledge Discovery and Data Mining ISBN: 9783642136566 PAKDD (1) |
| DOI: | 10.1007/978-3-642-13657-3_7 |
| Popis: | Integrative mining of heterogeneous data is one of the major challenges for data mining in the next decade. We address the problem of integrative clustering of data with mixed type attributes. Most existing solutions suffer from one or both of the following drawbacks: Either they require input parameters which are difficult to estimate, or/and they do not adequately support mixed type attributes. Our technique INTEGRATE is a novel clustering approach that truly integrates the information provided by heterogeneous numerical and categorical attributes. Originating from information theory, the Minimum Description Length (MDL) principle allows a unified view on numerical and categorical information and thus naturally balances the influence of both sources of information in clustering. Moreover, supported by the MDL principle, parameter-free clustering can be performed which enhances the usability of INTEGRATE on real world data. Extensive experiments demonstrate the effectiveness of INTEGRATE in exploiting numerical and categorical information for clustering. As an efficient iterative algorithm INTEGRATE is scalable to large data sets. |
| Databáze: | OpenAIRE |
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