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Outlier Detection Using a Novel method: Quantum Clustering [article]

Ding Liu, Hui Li
2020 arXiv   pre-print
And based on this hypothesis, we apply a novel density-based approach to unsupervised outlier detection.  ...  This approach, called Quantum Clustering (QC), deals with unlabeled data processing and constructs a potential function to find the centroids of clusters and the outliers.  ...  Different from these fundamental approaches, our approach in this paper, is a novel unsupervised Quantum Clustering technique [9] .  ... 
arXiv:2006.04760v1 fatcat:xvngybb7nzf5jcjilcdhwjbzh4

Quantum clustering algorithms

Esma Aïmeur, Gilles Brassard, Sébastien Gambs
2007 Proceedings of the 24th international conference on Machine learning - ICML '07  
In this paper, we initiate the idea of quantizing clustering algorithms by using variations on a celebrated quantum algorithm due to Grover.  ...  By the term "quantization", we refer to the process of using quantum mechanics in order to improve a classical algorithm, usually by making it go faster.  ...  However, in that case, we should begin by following the usual classical practice of detecting and removing those outliers before proceeding to divisive clustering.  ... 
doi:10.1145/1273496.1273497 dblp:conf/icml/AimeurBG07 fatcat:vi6wegmp7rb63n3tpcir4oxdbm

A Probabilistic framework for Quantum Clustering [article]

Raúl V. Casaña-Eslava, Paulo J. G. Lisboa, Sandra Ortega-Martorell, Ian H. Jarman, José D. Martín-Guerrero
2019 arXiv   pre-print
Quantum Clustering is a powerful method to detect clusters in data with mixed density. However, it is very sensitive to a length parameter that is inherent to the Schr\"odinger equation.  ...  We propose a probabilistic framework that provides an objective function for the goodness-of-fit to the data, enabling the control parameters to be optimised within a Bayesian framework.  ...  After obtaining the probability of cluster membership, the standard Bayesian framework can be used to detect outliers.  ... 
arXiv:1902.05578v1 fatcat:365ltndfpja2zpirugfyc5jupe

Quantum Statistical Information Grid Clustering for Early Esophageal Adenocarcinoma Detection

Vani G
2021 Revista GEINTEC  
Thus, in this present research work the statistical information-based grid clustering is developed by empowering its clustering capability using Quantum mechanism.  ...  The conventional STatistical INformation Grid Clustering (STING) reduces the computation complexity of the clustering process, but outlier detection and uncertainty handling are very challenging, because  ...  Li et al [7] designed a novel feature selection using sequential forward selection method. This work focusses on nonunique probe selection issue, the model is compared with greedy algorithm.  ... 
doi:10.47059/revistageintec.v11i4.2360 fatcat:v4aft4fqcvfpdpv2zy33wzlcl4

Locating Structural Centers: A Density-Based Clustering Method for Community Detection

Xiaofeng Wang, Gongshen Liu, Jianhua Li, Jan P. Nees, Yong Liu
2017 PLoS ONE  
The result of these experiments shows that the proposed method performs more efficiently with a comparative clustering performance than current state of the art methods.  ...  Local expanding methods have proven to be efficient and effective in community detection, but most methods are sensitive to initial seeds and built-in parameters.  ...  Acknowledgments The authors would like to thank Newman Mark, Alon U and Arenas A for providing realworld network datasets.  ... 
doi:10.1371/journal.pone.0169355 pmid:28046030 pmcid:PMC5207651 fatcat:tobzycwxzvag5hol6kryc3hjqy

Review on Clustering Algorithms Based on Data Type: Towards the Method for Data Combined of Numeric-Fuzzy Linguistics

L A Rasyid, S Andayani
2018 Journal of Physics, Conference Series  
Clustering groups a set of data object into clusters to maximize the similarity between objects in the same cluster and the dissimilarity between objects in the different clusters.  ...  The differences, the advantages as well as disadvantages of each algorithm and the opportunity to develop a clustering approach for combination of numeric and fuzzy linguistic are also discussed.  ...  The used method is surveying on results of research about clustering algorithms.  ... 
doi:10.1088/1742-6596/1097/1/012082 fatcat:f5ctr3p7zrgsdkz52tqtqfrhbu

Introducing AnomDB: An Unsupervised Anomaly Detection Method for CNC Machine Control Data

Lou Zhang, Sarah Elghazoly, Brock Tweedie
2019 Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM  
We perform experiments on CNC machine control data to demonstrate the effectiveness of this method in discovering anomalies over other commonly used methods of anomaly detection such as IQR and k-means  ...  clustering.  ...  DISCUSSION We propose AnomDB as a novel algorithm for detection of anomalous part cycles in CNC machines using their native control data in a live production setting.  ... 
doi:10.36001/phmconf.2019.v11i1.806 fatcat:d336unntxvhmlimsewy2i4htty

Aircraft Exhaust Gas Temperature Value Mining with Rough Set Method

Mustagime Tülin Yıldırım, Mehtap Taşcı
2021 International Journal of Aviation, Aeronautics, and Aerospace  
Li et al. (2015) applied clustering techniques to detect abnormal flights of unique data patterns.  ...  Yang et al ( 2014 ) studied a similar method to estimate degraded engine component parameters using quantum-behaved particle swarm optimization (QPSO) algorithm.  ... 
doi:10.15394/ijaaa.2021.1660 fatcat:myq7al3osrc57mz35fhqq3ijy4

Dynamic Granularity Matrix Space Based Adaptive Edge Detection Method for Structured Light Stripes

Changzhi Yu, Fang Ji, Xingjiu Jing, Mi Liu
2019 Mathematical Problems in Engineering  
In this paper, an adaptive Canny edge detection method with two phases is proposed for structured light stripes.  ...  Secondly, a quantum-inspired group leader hybrid algorithm is adopted to calculate the optimal threshold from the optimal granularity layer, which is taken as the adaptive threshold for Canny operator.  ...  Conclusion In this work, addressing the edge detection problem of structured light stripes under the conditions of high illumination, we introduced a novel adaptive edge detection method with two phases  ... 
doi:10.1155/2019/1959671 fatcat:7uba5fy6lzfrnfj4yq5wklqet4

Big Data Reduction Methods: A Survey

Muhammad Habib ur Rehman, Chee Sun Liew, Assad Abbas, Prem Prakash Jayaraman, Teh Ying Wah, Samee U. Khan
2016 Data Science and Engineering  
This article presents a review of methods that are used for big data reduction.  ...  learning methods.  ...  Jalali and Asghari [47] AST The AST is a novel method that is used to compress digital signal by performing selective stretching and wrapping methods.  ... 
doi:10.1007/s41019-016-0022-0 fatcat:3ivz52kpz5dhratokm4uenuoc4

Survey on Botnet Detection Techniques: Classification, Methods, and Evaluation

Ying Xing, Hui Shu, Hao Zhao, Dannong Li, Li Guo, Jude Hemanth
2021 Mathematical Problems in Engineering  
Combing with expert scores and objective weights, this survey proposes quantitative evaluation and gives a visual representation for typical detection methods.  ...  From the four dimensions of service, intelligence, collaboration, and assistant, a common bot detection evaluation system (CBDES) is proposed, which defines a new global capability measurement standard  ...  Based on the topological features of the nodes in the graph, a novel botnet detection method was proposed, which extracts in-degree, out-degree, weight, degree weight, clustering coefficient, internodes  ... 
doi:10.1155/2021/6640499 fatcat:hkafnnj2cnbzjdbuk6iel3b5cm

A Distribution Separation Method Using Irrelevance Feedback Data for Information Retrieval

Peng Zhang, Qian Yu, Yuexian Hou, Dawei Song, Jingfei Li, Bin Hu
2017 ACM Transactions on Intelligent Systems and Technology  
., pseudo) irrelevance feedback, by automatically detecting the seed irrelevant documents via three different document re-ranking methods.  ...  This paper is focused on the application in Information Retrieval, where relevance feedback is a widely used technique to build a refined query model based on a set of feedback documents.  ...  As for the irrelevant documents detection methods, we use the Outlier-based method (Outlier), QPRP-based method (QPRP), and L2R-based method (L2R), described in Section 5.3.1.  ... 
doi:10.1145/2994608 fatcat:3rhncljibbbb7bssxecpid4dbi

Method development of glycoprotein biomarkers for cancers

Shuang Yang, Perry G Wang
2017 Bioanalysis  
Results: A multiplatform comparison of serum of smokers (n = 55) and never-smokers (n = 57) using nuclear magnetic resonance spectroscopy, UPLC-MS and statistical modeling revealed clustering of the classes  ...  The identified metabolites were subjected to metabolic pathway enrichment, modeling adverse biological events using available databases.  ...  The raw mass spectrometric data acquired were processed using xcms in R [28] and the centwave peak picking methods were used to detect chromatographic peaks.  ... 
doi:10.4155/bio-2017-0077 pmid:28644045 fatcat:qiae7o5ukzgibl6vyrhswsqeqi

Computational Methods in Drug Discovery

G. Sliwoski, S. Kothiwale, J. Meiler, E. W. Lowe
2013 Pharmacological Reviews  
A statistical clustering of observed side-chain conformations in PDB, called a rotamer library, is used in most side-chain construction methods Computational Methods in Drug Discovery (Krivov et al.,  ...  Geometric method. Geometric algorithms identify binding sites through the detection of cavities on a protein's surface.  ... 
doi:10.1124/pr.112.007336 pmid:24381236 pmcid:PMC3880464 fatcat:4dzrdkspkjecnombnchznma2ny

QuPWM: Feature Extraction Method for Epileptic Spike Classification

Abderrazak Chahid, Fahad Albalawi, Turky Alotaiby, Majed Hamad Al-Hameed, Saleh A. Alshebeili, Taous-Meriem Laleg-Kirati
2020 IEEE journal of biomedical and health informatics  
First, we used the Position Weight Matrix (PWM) method combined with a uniform quantizer to generate useful features from time domain and frequency domain through a Fast Fourier Transform (FFT) of the  ...  The common practice for Inter-ictal spike detection of brain signals is via visual scanning of the recordings, which is a subjective and a very time-consuming task.  ...  In this work, we propose a novel feature generation method for a multi-channel MEG signal, which improves patientindependent inter-ictal spike detection.  ... 
doi:10.1109/jbhi.2020.2972286 pmid:32054592 fatcat:wvbwdllfdfbidplogou6lkbmce
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