Algorithms for clustering data
Algorithms for clustering data
A new approach to effective circuit clustering
ICCAD '92 1992 IEEE/ACM international conference proceedings on Computer-aided design
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
ACM Computing Surveys (CSUR)
Clustering spatial data using random walks
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
ROCK: A Robust Clustering Algorithm for Categorical Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Clustering Spatial Data Using Random Walks,
Clustering Spatial Data Using Random Walks,
On Contracting Graphs to Fixed Pattern Graphs
SOFSEM '10 Proceedings of the 36th Conference on Current Trends in Theory and Practice of Computer Science
Distributed graph clustering for application in wireless networks
IWSOS'11 Proceedings of the 5th international conference on Self-organizing systems
Simultaneous similarity learning and feature-weight learning for document clustering
TextGraphs-6 Proceedings of TextGraphs-6: Graph-based Methods for Natural Language Processing
On graph contractions and induced minors
Discrete Applied Mathematics
GANC: Greedy agglomerative normalized cut for graph clustering
Pattern Recognition
An empirical approach to the measurement of interchromosomal distances in the genetic algorithm
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Ensemble partitioning for unsupervised image categorization
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Quantum speed-up for unsupervised learning
Machine Learning
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We propose a novel approach to clustering, based on deterministic analysis of random walks on the weighted graph associated with the clustering problem. The method is centered around what we shall call separating operators, which are applied repeatedly to sharpen the distinction between the weights of inter-cluster edges (the so-called separators), and those of intra-cluster edges. These operators can be used as a stand-alone for some problems, but become particularly powerful when embedded in a classical multi-scale framework and/or enhanced by other known techniques, such as agglomerative clustering. The resulting algorithms are simple, fast and general, and appear to have many useful applications.