Strange Bedfellows: Quantum Mechanics and Data Mining

Weinstein, Marvin

doi:10.1016/j.nuclphysbps.2010.02.009

Cited by 4 publications

(1 citation statement)

References 4 publications

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“…Dynamic methods address this challenge by attempting to determine if there are multiple ways of clustering the data. For example, hierarchical methods can discover super patterns between different clusters or find sub-clusters within a single cluster [28,27,29,19].…”

Section: Dynamic Quantum Clusteringmentioning

confidence: 99%

Enhanced Dynamic Quantum Clustering Based on von Neumann Entropy

Sadrnezhaad

Loo

2023

Preprint

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Dynamic quantum clustering (DQC) is a quantum algorithm to find possible data clusters. DQC uses quantum states to represent the clusters and the time evolution of the quantum states to predict different ways to match the data to clusters. There are several advantages to this method, as: (1) there is no need to specify the number of clusters; (2) there is no need to reduce the data; (3) hidden patterns within the main pattern can be studied; and (4) it eliminates the need for an operator to determine the point at which to terminate the process visually through the effective implementation of a fitness function. This paper introduces von Neumann entropy as a valuable metric that can be used to evaluate the DQC algorithm's results. Enhanced DQC can also show if there are different possible ways to cluster the data.

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Section: Dynamic Quantum Clusteringmentioning

confidence: 99%

Enhanced Dynamic Quantum Clustering Based on von Neumann Entropy

Sadrnezhaad

Loo

2023

Preprint

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Analyzing documents with Quantum Clustering: A novel pattern recognition algorithm based on quantum mechanics

Liu

Jiang

Yang

et al. 2016

Pattern Recognition Letters

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Physics-Based Anomaly Detection Defined on Manifold Space

Huang

Qin

Yoo

et al. 2014

ACM Trans. Knowl. Discov. Data

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Current popular anomaly detection algorithms are capable of detecting global anomalies but often fail to distinguish local anomalies from normal instances. Inspired by contemporary physics theory (i.e., heat diffusion and quantum mechanics), we propose two unsupervised anomaly detection algorithms. Building on the embedding manifold derived from heat diffusion, we devise Local Anomaly Descriptor (LAD), which faithfully reveals the intrinsic neighborhood density. It uses a scale-dependent umbrella operator to bridge global and local properties, which makes LAD more informative within an adaptive scope of neighborhood. To offer more stability of local density measurement on scaling parameter tuning, we formulate Fermi Density Descriptor (FDD), which measures the probability of a fermion particle being at a specific location. By choosing the stable energy distribution function, FDD steadily distinguishes anomalies from normal instances with any scaling parameter setting. To further enhance the efficacy of our proposed algorithms, we explore the utility of anisotropic Gaussian kernel (AGK), which offers better manifold-aware affinity information. We also quantify and examine the effect of different Laplacian normalizations for anomaly detection. Comprehensive experiments on both synthetic and benchmark datasets verify that our proposed algorithms outperform the existing anomaly detection algorithms.

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Strange Bedfellows: Quantum Mechanics and Data Mining

Cited by 4 publications

References 4 publications

Enhanced Dynamic Quantum Clustering Based on von Neumann Entropy

Enhanced Dynamic Quantum Clustering Based on von Neumann Entropy

Analyzing documents with Quantum Clustering: A novel pattern recognition algorithm based on quantum mechanics

Physics-Based Anomaly Detection Defined on Manifold Space

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