Algorithms for clustering data
Algorithms for clustering 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
Fast algorithms for projected clustering
SIGMOD '99 Proceedings of the 1999 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
Finding generalized projected clusters in high dimensional spaces
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Adaptive dimension reduction for clustering high dimensional data
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Computing Clusters of Correlation Connected objects
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
HARP: A Practical Projected Clustering Algorithm
IEEE Transactions on Knowledge and Data Engineering
On Discovery of Extremely Low-Dimensional Clusters Using Semi-Supervised Projected Clustering
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
ACM Transactions on Knowledge Discovery from Data (TKDD)
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Finding correlation clusters in the arbitrary subspaces of high- dimensional data is an important and a challenging research problem. The current state-of-the-art correlation clustering approaches are sensitive to the initial set of seeds chosen and do not yield the optimal result in the presence of noise. To avoid these problems, we propose RObust SEedless Correlation Clustering (ROSECC) algorithm that does not require the selection of the initial set of seeds. Our approach incrementally partitions the data in each iteration and applies PCA to each partition independently. ROSECC does not assume the dimensionality of the cluster beforehand and automatically determines the appropriate dimensionality (and the corresponding subspaces) of the correlation cluster. Experimental results on both synthetic and real-world datasets demonstrate the effectiveness of the proposed method. We also show the robustness of our method in the presence of a significant noise levels in the data.