6533b857fe1ef96bd12b468f
RESEARCH PRODUCT
Quantum clustering in non-spherical data distributions: Finding a suitable number of clusters
Raul V. Casana-eslavaJosé D. Martín-guerreroPaulo J. G. LisboaIan H. Jarmansubject
0301 basic medicineClustering high-dimensional dataMathematical optimizationCognitive NeuroscienceSingle-linkage clusteringCorrelation clustering02 engineering and technologyComputer Science ApplicationsHierarchical clusteringDetermining the number of clusters in a data set03 medical and health sciences030104 developmental biologyArtificial Intelligence0202 electrical engineering electronic engineering information engineeringCluster (physics)020201 artificial intelligence & image processingQACluster analysisAlgorithmk-medians clusteringMathematicsdescription
Quantum Clustering (QC) provides an alternative approach to clustering algorithms, several of which are based on geometric relationships between data points. Instead, QC makes use of quantum mechanics concepts to find structures (clusters) in data sets by finding the minima of a quantum potential. The starting point of QC is a Parzen estimator with a fixed length scale, which significantly affects the final cluster allocation. This dependence on an adjustable parameter is common to other methods. We propose a framework to find suitable values of the length parameter σ by optimising twin measures of cluster separation and consistency for a given cluster number. This is an extension of the Separation and Concordance framework previously introduced for K-means clustering. Experimental results on two synthetic data sets and three challenging real-world data sets show that optimisation of cluster separation identifies QC solutions with consistently high Jaccard score measured against true-cluster labels while optimisation of cluster consistency provides insights into hierarchical cluster structure. © 2017 Elsevier B.V.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2017-12-01 | Neurocomputing |