A new algorithm for clustering based on kernel density estimation

Matioli, Luiz Carlos; Santos, Sérgio Rodrigo Quadros dos; Kleina, Mariana; Leite, Eduardo Alvim

doi:10.1080/02664763.2016.1277191

Cited by 44 publications

(37 citation statements)

References 20 publications

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“…Multi-dimensional Kernel Density Estimation (MKDE) clustering based anomaly detection is a modified approach for anomaly detection via non-parametric density estimation for clustering [11]. It has the advantage that it does not require a priori knowledge of the number of clusters.…”

Section: Multi-dimensional Kernel Density Estimationmentioning

confidence: 99%

“…A rather simpler approach is to limit the number of extracted feature vectors (principal components) and to subject them onto the proposed multidimensional kernel density estimation (MKDE) based clustering algorithm for further evaluation in the second step. The proposed MKDE clustering helps in grouping the data into clusters (only normal trades) without asking the number of clusters up front [11]. The major advantages of using this approach is its decision-making capability based on analyzing the patterns that are subjected being an anomaly without prior information about the location or the nature of the manipulation and also helps in reducing the total amount of computations.…”

Section: Introductionmentioning

confidence: 99%

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Detection of Stock Price Manipulation Using Kernel Based Principal Component Analysis and Multivariate Density Estimation

et al. 2020

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Stock price manipulation uses illegitimate means to artificially influence market prices of several stocks. It causes massive losses and undermines investors' confidence and the integrity of the stock market. Several existing research works focused on detecting a specific manipulation scheme using supervised learning but lacks the adaptive capability to capture different manipulative strategies. This begets the assumption of model parameter values specific to the underlying manipulation scheme. In addition, supervised learning requires the use of labelled data which is difficult to acquire due to confidentiality and the proprietary nature of trading data. The proposed research establishes a detection model based on unsupervised learning using Kernel Principal Component Analysis (KPCA) and applied increased variance of selected latent features in higher dimensions. A proposed Multidimensional Kernel Density Estimation (MKDE) clustering is then applied upon the selected components to identify abnormal patterns of manipulation in data. This research has an advantage over the existing methods in overcoming the ambiguity of assuming values of several parameters, reducing the high dimensions obtained from conventional KPCA and thereby reducing computational complexity. The robustness of the detection model has also been evaluated when two or more manipulative activities occur within a short duration of each other and by varying the window length of the dataset fed to the model. Validation on multiple datasets and a comprehensive assessment of the model performance has been conducted without providing any prior information about the location of the manipulation. The results show a significant performance enhancement in terms of the Fmeasure values and a significant reduction in false alarm rate (FAR) has been achieved.

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Section: Multi-dimensional Kernel Density Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection of Stock Price Manipulation Using Kernel Based Principal Component Analysis and Multivariate Density Estimation

et al. 2020

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show abstract

“…A joint delay and angular clustering method such as KDE has been used in [3] to make identification of multipath clusters more realistic and practical. Recently, authors in [28] proposed a new algorithm for clustering measurement data based on KDE. In this paper, a Gaussian KDE is proposed to estimate the MPCs clusters in an indoor environment.…”

Section: Direction Of Rx Placement From Txmentioning

confidence: 99%

Spatiotemporal Statistical Channel Model for Indoor Corridor at 14 GHz, 18 GHz, and 22 GHz Bands

Oyie

Afullo

2018

Wireless Communications and Mobile Computing

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Several techniques have been proposed to overcome challenges of meeting demands for higher data rates in wireless communication. Space-time diversity method is proposed to exploit spatiotemporal nature of the channel; hence, a comprehensive knowledge of the spatiotemporal properties of a channel is required. In this paper, a measurement-based channel model that considers both delay and angular domains of an indoor corridor channel for 14 GHz, 18 GHz, and 22 GHz is proposed. A nonparametric Gaussian kernel density estimation method is applied for cluster identification for the three frequency bands. This work proposes a spatiotemporal model that conditions the model parameters on the azimuthal spatial domain. The clusters are modeled on the complete azimuth plane and a Gaussian estimation distribution is fitted onto the empirical data plot. Both clusters and multipath components are modeled and results are compared with Saleh-Valenzuela model parameter values. The results show that both clusters and multipath components can be estimated by probability density functions that follow Gaussian and Laplacian fits on the spatial domain for indoor corridor environment, respectively.

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“…В предлагаемой работе для оценки количества кластеров в исследуемой выборке пациентов с аМКС использовали алгоритм кластеризации на основе одномерной оценки плотности ядра (kernel density estimation), а для распределения объектов по кластерам использовали итеративный алгоритм k-средних (k-means) [12][13][14].…”

Section: материал и методыunclassified

The use of cluster analysis and logistic regression for assessing the risk of Alzheimer's disease in patients with mild cognitive impairment, amnestic type

Симонов

Klyushnik

Андросова

et al. 2018

Z. nevrol. psikhiatr. im. S.S. Korsakova

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A new algorithm for clustering based on kernel density estimation

Cited by 44 publications

References 20 publications

Detection of Stock Price Manipulation Using Kernel Based Principal Component Analysis and Multivariate Density Estimation

Detection of Stock Price Manipulation Using Kernel Based Principal Component Analysis and Multivariate Density Estimation

Spatiotemporal Statistical Channel Model for Indoor Corridor at 14 GHz, 18 GHz, and 22 GHz Bands

The use of cluster analysis and logistic regression for assessing the risk of Alzheimer's disease in patients with mild cognitive impairment, amnestic type

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