Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
ACM Computing Surveys (CSUR)
Computable analysis: an introduction
Computable analysis: an introduction
Cure: an efficient clustering algorithm for large databases
Information Systems
On Clustering Validation Techniques
Journal of Intelligent Information Systems
Real number computation through gray code embedding
Theoretical Computer Science
WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
The Art of Computer Programming, Volume 4, Fascicle 2: Generating All Tuples and Permutations (Art of Computer Programming)
Compression-based data mining of sequential data
Data Mining and Knowledge Discovery
SPARCL: an effective and efficient algorithm for mining arbitrary shape-based clusters
Knowledge and Information Systems
Multi-dimensional Mass Estimation and Mass-based Clustering
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
IEEE Transactions on Information Theory
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We propose new approaches to exploit compression algorithms for clustering numerical data. Our first contribution is to design a measure that can score the quality of a given clustering result under the light of a fixed encoding scheme. We call this measure the Minimum Code Length (MCL). Our second contribution is to propose a general strategy to translate any encoding method into a cluster algorithm, which we call COOL (COding-Oriented cLustering). COOL has a low computational cost since it scales linearly with the data set size. The clustering results of COOL is also shown to minimize MCL. To illustrate further this approach, we consider the Gray Code as the encoding scheme to present GCOOL. G-COOL can find clusters of arbitrary shapes and remove noise. Moreover, it is robust to change in the input parameters; it requires only two lower bounds for the number of clusters and the size of each cluster, whereas most algorithms for finding arbitrarily shaped clusters work well only if all parameters are tuned appropriately. G-COOL is theoretically shown to achieve internal cohesion and external isolation and is experimentally shown to work well for both synthetic and real data sets.