论文信息 - A Density-Biased Sampling Technique to Improve Cluster Representativeness

A Density-Biased Sampling Technique to Improve Cluster Representativeness

The volume and complexity of data collected by modern applications has grown significantly, leading to increasingly costly operations for both data manipulation and analysis. Sampling is an useful technique to support manager a more sensible volume in the data reduction process. Uniform sampling has been widely used but, in datasets exhibiting skewed cluster distribution, biased sampling shows better results. This paper presents the BBS - Biased Box Samplingalgorithm which aims at keeping the skewed tendency of the clusters from the original data. We also present experimental results obtained with the proposed BBS algorithm.

Ana Paula Appel | Agma J. M. Traina | Caetano Traina | Elaine P. M. de Sousa | Adriano Arantes Paterlini

[1] Yannis Manolopoulos,et al. Indexed-based density biased sampling for clustering applications , 2006, Data Knowl. Eng..

[2] Pairote Sattayatham,et al. Density-biased clustering based on reservoir sampling , 2005, 16th International Workshop on Database and Expert Systems Applications (DEXA'05).

[3] Hans-Peter Kriegel,et al. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[4] Ana Paula Appel,et al. Biased box sampling - a density-biased sampling for clustering , 2007, SAC '07.

[5] Christos Faloutsos,et al. Density biased sampling: an improved method for data mining and clustering , 2000, SIGMOD 2000.

[6] Stefan Berchtold,et al. Efficient Biased Sampling for Approximate Clustering and Outlier Detection in Large Data Sets , 2003, IEEE Trans. Knowl. Data Eng..