Scalable algorithms for clustering large datasets with mixed type attributes
✍ Scribed by Zengyou He; Xiaofei Xu; Shengchun Deng
- Publisher
- John Wiley and Sons
- Year
- 2005
- Tongue
- English
- Weight
- 308 KB
- Volume
- 20
- Category
- Article
- ISSN
- 0884-8173
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✦ Synopsis
Clustering is a widely used technique in data mining applications for discovering patterns in underlying data. Most traditional clustering algorithms are limited to handling datasets that contain either numeric or categorical attributes. However, datasets with mixed types of attributes are common in real life data mining applications. In this article, we present two algorithms that extend the Squeezer algorithm to domains with mixed numeric and categorical attributes. The performance of the two algorithms has been studied on real and artificially generated datasets. Comparisons with other clustering algorithms illustrate the superiority of our approaches.