[1] Lu Yansheng, Hu Rong, Zou Lei, Zhou Chong, et al. Mining maximal pattern-based subspace clustersin high dimensional space [J]. Journal of Southeast University (English Edition), 2006, 22 (4): 490-495. [doi:10.3969/j.issn.1003-7985.2006.04.010]
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Mining maximal pattern-based subspace clustersin high dimensional space(
)
高维空间基于样式相似性的最大子空间聚类
)Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]
- Volumn:
- 22
- Issue:
- 2006 4
- Page:
- 490-495
- Research Field:
- Computer Science and Engineering
- Publishing date:
- 2006-12-30
Info
- Title:
- Mining maximal pattern-based subspace clustersin high dimensional space
- 高维空间基于样式相似性的最大子空间聚类
- Author(s):
- Lu Yansheng; Hu Rong; Zou Lei; Zhou Chong
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
- PACS:
- TP311
- DOI:
- 10.3969/j.issn.1003-7985.2006.04.010
- Abstract:
- The problem of pattern-based subspace clustering, a special type of subspace clustering that uses pattern similarity as a measure of similarity, is studied.Unlike most traditional clustering algorithms that group the close values of objects in all the dimensions or a set of dimensions, clustering by pattern similarity shows an interesting pattern, where objects exhibit a coherent pattern of rise and fall in subspaces.A novel approach, named EMaPle to mine the maximal pattern-based subspace clusters, is designed.The EMaPle searches clusters only in the attribute enumeration spaces which are relatively few compared to the large number of row combinations in the typical datasets, and it exploits novel pruning techniques.EMaPle can find the clusters satisfying coherent constraints, size constraints and sign constraints neglected in MaPle.Both synthetic data sets and real data sets are used to evaluate EMaPle and demonstrate that it is more effective and scalable than MaPle.
- 研究了基于样式相似性的子空间聚类问题, 使用样式相似性作为相似性度量.与在所有维或者子维集上聚集距离相近的对象的传统聚类方法不同的是, 样式相似性寻找的是这样一种有趣的样式:对象在子维上呈现出相同起伏的一致变化.提出了一种挖掘基于样式相似性的最大子空间聚类的方法EMaPle.一般情况下数据集属性数目远小于对象数目, 因此仅在属性计数空间查找簇, 然后运用一些修剪策略.该方法能够找到同时满足一致性约束、大小约束和被MaPle忽视了的符号约束的聚类.在合成和实际数据集上的实验结果表明该算法优于原来的MaPle算法.
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Memo
- Memo:
- Biography: Lu Yansheng(1949—), male, professor, lys@mail.hust.edu.cn.