Algorithms for clustering data
Algorithms for clustering data
Optimal algorithms for approximate clustering
STOC '88 Proceedings of the twentieth annual ACM symposium on Theory of computing
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Polynomial time approximation schemes for dense instances of NP-hard problems
STOC '95 Proceedings of the twenty-seventh annual ACM symposium on Theory of computing
Approximation algorithms for geometric problems
Approximation algorithms for NP-hard problems
Approximation algorithms for facility location problems (extended abstract)
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Journal of Algorithms
A Microeconomic View of Data Mining
Data Mining and Knowledge Discovery
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
An information-theoretic analysis of hard and soft assignment methods for clustering
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Upper bound for the approximation ratio of a class of hypercube segmentation algorithms
Information Processing Letters
Customer-oriented catalog segmentation: effective solution approaches
Decision Support Systems
Nestedness and segmented nestedness
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A search space reduction methodology for data mining in large databases
Engineering Applications of Artificial Intelligence
Stochastic Submodular Maximization
WINE '08 Proceedings of the 4th International Workshop on Internet and Network Economics
APPROX '09 / RANDOM '09 Proceedings of the 12th International Workshop and 13th International Workshop on Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques
Upper bound for the approximation ratio of a class of hypercube segmentation algorithms
Information Processing Letters
A search space reduction methodology for large databases: a case study
ICDM'07 Proceedings of the 7th industrial conference on Advances in data mining: theoretical aspects and applications
On the complexity of several haplotyping problems
WABI'05 Proceedings of the 5th International conference on Algorithms in Bioinformatics
Analogy-based reasoning in classifier construction
Transactions on Rough Sets IV
TreeMatrix: A Hybrid Visualization of Compound Graphs
Computer Graphics Forum
An automated search space reduction methodology for large databases
ICDM'13 Proceedings of the 13th international conference on Advances in Data Mining: applications and theoretical aspects
Multi-winner social choice with incomplete preferences
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
| Hi-index | 0.01 |
We study a novel genre of optimization problems, which we call segmentation problems, motivated in part by certain aspects of clustering and data mining. For any classical optimization problem, the corresponding segmentation problem seeks to partition a set of cost vectors into several segments, so that the overall cost is optimized. We focus on two natural and interesting (but MAXSNP-complete) problems in this class, the hypercube segmentation problem and the catalog segmentation problem, and present approximation algorithms for them. We also present a general greedy scheme, which can be specialized to approximate any segmentation problem.