open access

https://hdl.handle.net/2115/48457

Multi-Dimensional Mass Estimation and Mass-based Clustering

英語

2011-06

ERATO湊離散構造処理系プロジェクト

ERATOセミナ2010 : No.28. 2010年11月19日

Mass estimation, an alternative to density estimation, has been shown recently to be an e ective base modelling mechanism for three data mining tasks of regression, information retrieval and anomaly detection. This talk reports two advancements of the first work on mass estimation. First, we generalize the previously proposed one-dimensional mass estimation to multidimensional mass estimation, and significantly reduce the time complexity to O( h) from O( h)-making it feasible for a full range of generic problems. Second, we introduce the first clustering method based on mass-it is unique because it does not employ any distance or density measure. The structure of the new mass model enables di erent parts of a cluster to be identified and merged without expensive evaluations. The characteristics of the new clustering method are: (i) it can identify arbitraryshape clusters; (ii) it is significantly faster than existing density-based or distance-based methods; and (iii) it is noise-tolerant.

会議発表資料

Not Applicable (or Unknown)

2186-1846