Dempster's rule of combination is #P-complete (research note)
Artificial Intelligence
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Entropy-based subspace clustering for mining numerical data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
BIRCH: A New Data Clustering Algorithm and Its Applications
Data Mining and Knowledge Discovery
CLARANS: A Method for Clustering Objects for Spatial Data Mining
IEEE Transactions on Knowledge and Data Engineering
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
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Due to inherent sparse, noise and nearly zero difference characteristics of high dimensional data sets, traditional clustering methods fails to detect meaningful clusters in them. Subspace clustering attempts to find the true distribution inherent to the subsets with original attributes. However, which subspace contains the true clustering result is usually uncertain. From this point of view, subspace clustering can be regarded as an uncertain discursion problem. In this paper, we firstly develop the criterion to evaluate creditable subspaces which contain the meaningful clustering results, and then propose a creditable subspace labeling method (CSL) based on D-S evidence theory. The creditable subspaces of the original data space can be found by iteratively executing the algorithm CSL. Once the creditable subspaces are got, the true clustering results can be found using a traditional clustering algorithm on each creditable subspace. Experiments show that CSL can detect the actual creditable subspace with the original attribute. In this way, a novel approach of clustering problems using traditional clustering algorithms to deal with high dimension data sets is proposed.