Vertical partitioning algorithms for database design
ACM Transactions on Database Systems (TODS)
Marketing applications of sequencing and partitioning of nonsymmetric and/or two-mode matrices
Data, expert knowledge and decisions
Exact solution of large-scale, asymmetric traveling salesman problems
ACM Transactions on Mathematical Software (TOMS)
Principles of distributed database systems (2nd ed.)
Principles of distributed database systems (2nd ed.)
Techniques for Structuring Database Records
ACM Computing Surveys (CSUR)
Data Mining: Introductory and Advanced Topics
Data Mining: Introductory and Advanced Topics
TSP Cuts Which Do Not Conform to the Template Paradigm
Computational Combinatorial Optimization, Optimal or Provably Near-Optimal Solutions [based on a Spring School]
Deriving Program Physical Structures Using Bond Energy Algorithm
APSEC '99 Proceedings of the Sixth Asia Pacific Software Engineering Conference
Multi-way graph and hypergraph partitioning
Multi-way graph and hypergraph partitioning
Take a walk and cluster genes: a TSP-based approach to optimal rearrangement clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Cut-and-solve: an iterative search strategy for combinatorial optimization problems
Artificial Intelligence
Compressing large boolean matrices using reordering techniques
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Extension de DataTube pour la fouille visuelle de données temporelles
Proceedings of the 20th International Conference of the Association Francophone d'Interaction Homme-Machine
Journal of Global Optimization
Visual Mining of Web Logs with DataTube2
WISE '09 Proceedings of the 10th International Conference on Web Information Systems Engineering
Detecting community structure in complex networks by optimal rearrangement clustering
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
A network flow model for biclustering via optimal re-ordering of data matrices
Journal of Global Optimization
An ACO based functional module detection algorithm for protein interaction networks
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Employing heat maps to mine associations in structured routine care data
Artificial Intelligence in Medicine
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Given a matrix of values in which the rows correspond to objects and the columns correspond to features of the objects, rearrangement clustering is the problem of rearranging the rows of the matrix such that the sum of the similarities between adjacent rows is maximized. Referred to by various names and reinvented several times, this clustering technique has been extensively used in many fields over the last three decades. In this paper, we point out two critical pitfalls that have been previously overlooked. The first pitfall is deleterious when rearrangement clustering is applied to objects that form natural clusters. The second concerns a similarity metric that is commonly used. We present an algorithm that overcomes these pitfalls. This algorithm is based on a variation of the Traveling Salesman Problem. It offers an extra benefit as it automatically determines cluster boundaries. Using this algorithm, we optimally solve four benchmark problems and a 2,467-gene expression data clustering problem. As expected, our new algorithm identifies better clusters than those found by previous approaches in all five cases. Overall, our results demonstrate the benefits of rectifying the pitfalls and exemplify the usefulness of this clustering technique. Our code is available at our websites.