Approximation schemes for covering and packing problems in image processing and VLSI
Journal of the ACM (JACM)
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
The budgeted maximum coverage problem
Information Processing Letters
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data mining: concepts and techniques
Data mining: concepts and techniques
Algorithms for facility location problems with outliers
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
Two-phase clustering process for outliers detection
Pattern Recognition Letters
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Spectral partitioning works: planar graphs and finite element meshes
FOCS '96 Proceedings of the 37th Annual Symposium on Foundations of Computer Science
On convergence properties of the em algorithm for gaussian mixtures
Neural Computation
Robust clustering by pruning outliers
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robust clustering methods: a unified view
IEEE Transactions on Fuzzy Systems
Gaussian mixture density modeling, decomposition, and applications
IEEE Transactions on Image Processing
On approximating four covering and packing problems
Journal of Computer and System Sciences
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In this paper we present the cluster identification of molecules (CIM), which is a clustering problem in a finite metric space. We model the problem as a parameter estimation via likelihood maximization and as a novel clustering problem, the maximum profit coverage problem (MPCP). We present a numerical study in which we compare a greedy heuristic and a random heuristic for MPCP, to the known Expectation Minimization approach for the likelihood maximization model. We present a polynomial time approximation scheme for MPCP in Euclidean space.