Cartification: A Neighborhood Preserving Transformation for Mining High Dimensional Data
| Autor: | Emmanuel Müller, Emin Aksehirli, Jilles Vreeken, Bart Goethals |
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| Rok vydání: | 2013 |
| Předmět: |
Computer. Automation
Clustering high-dimensional data Fuzzy clustering business.industry Correlation clustering Pattern recognition computer.software_genre Knowledge extraction Consensus clustering FLAME clustering Data mining Artificial intelligence business Cluster analysis computer Curse of dimensionality Mathematics |
| Zdroj: | ICDM Data Mining (ICDM) : 2013 IEEE 13th International Conference on Data Mining, 7-10 December 2013, Dallas, Texas, USA |
| DOI: | 10.1109/icdm.2013.146 |
| Popis: | The analysis of high dimensional data comes with many intrinsic challenges. In particular, cluster structures become increasingly hard to detect when the data includes dimensions irrelevant to the individual clusters. With increasing dimensionality, distances between pairs of objects become very similar, and hence, meaningless for knowledge discovery. In this paper we propose Cartification, a new transformation to circumvent this problem. We transform each object into an item set, which represents the neighborhood of the object. We do this for multiple views on the data, resulting in multiple neighborhoods per object. This transformation enables us to preserve the essential pair wise-similarities of objects over multiple views, and hence, to improve knowledge discovery in high dimensional data. Our experiments show that frequent item set mining on the certified data outperforms competing clustering approaches on the original data space, including traditional clustering, random projections, principle component analysis, subspace clustering, and clustering ensemble. |
| Databáze: | OpenAIRE |
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