Automated Determination of the Input Parameter of DBSCAN Based on Outlier Detection

Akbari, Zohreh; Unland, Rainer

doi:10.1007/978-3-319-44944-9_24

Cited by 15 publications

(6 citation statements)

References 13 publications

(8 reference statements)

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“…In the frequency domain, the Hilbert transform simply adds a phase shift of 90° to the phase spectrum of the Fourier transform. The application of the Hilbert transform to a seismic trace, x ( t ), produces a complex seismic trace z ( t ), as follows: z ( t ) = x ( t )+ i y ( t ) where y ( t ) is the seismic trace rotated by 90° produced by the Hilbert transform [ 14 ]. The envelope is calculated as follows:

…”

Section: Methodsmentioning

confidence: 99%

First Arrival Picking of Zero-Phase Seismic Data by Hilbert Envelope Empirical Half Window (HEEH) Method

Bargees

Al-Shuhail

2022

Sensors

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First arrival travel time picking is an important step in many seismic data-processing applications. Most first arrival picking methods search for a sudden jump in seismic energy at trace onsets, which is clearly appropriate for minimum-phase data. This paper proposes a method for the first arrival picking of non-minimum phase data based on complex trace analysis. The Hilbert integral transform generates a complex seismic trace, followed by extraction of the envelope. The first arrival identification introduces an outlier detection method that uses the widely used three-sigma rule of thumb, which is commonly used in most software algorithms to identify outliers. The proposed method ultimately generates logical windows of ones (at the locations of outliers) and zeros (elsewhere). The first arrival is selected in the middle of the first outlier window. Testing the proposed method on zero-phase synthetic data with added 10% and 20% random noise, the method detected the true first arrivals accurately. Furthermore, tests on real Vibroseis data showed that the method recognizes the first arrivals with 67% accuracy within 20 milliseconds of their corresponding arrival times manually picked by an experienced geophysicist.

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…”

Section: Methodsmentioning

confidence: 99%

First Arrival Picking of Zero-Phase Seismic Data by Hilbert Envelope Empirical Half Window (HEEH) Method

Bargees

Al-Shuhail

2022

Sensors

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“…An algorithm to fnd Eps automatically is proposed by Akbari and Unland, thereby eliminating human interaction in [5]. Comparison between the original DBSCAN and the proposed algorithm is done on normal distribution artifcial datasets.…”

Section: Literature Surveymentioning

confidence: 99%

“…Suppose there are a few data points (5,4), (2,6), (13, 3), (8, 7), (3,1). For simplicity, 2 Dimensional data are considered.…”

Section: Input: Ds Eps Minsptsmentioning

confidence: 99%

A Machine Learning Approach for Environmental Assessment on Air Quality and Mitigation Strategy

Shetty,

Seema,

Sowmya

et al. 2024

Journal of Engineering

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Air pollution has a significant impact on environment resulting in consequences such as global warming and acid rain. Toxic emissions from vehicles are one of the primary sources of pollution. Assessment of air pollution data is critical in order to assist residents in locating the safest areas in the city that are ideal for life. In this work, density-based spatial clustering of applications with noise (DBSCAN) is used which is among the widely used clustering algorithms in machine learning. It is not only capable of finding clusters of various sizes and shapes but can also detect outliers. DBSCAN takes in two important input parameters—Epsilon (Eps) and Minimum Points (MinPts). Even the slightest of variations in the parameter values fed to DBSCAN makes a big difference in the clustering. There is a need to find Eps value in as minimum time as possible. In this work, the goal is to find the Eps value in less time. For this purpose, a search tree technique is used for finding the Eps input to the DBSCAN algorithm. Predicting air pollution is a complex task due to various challenges associated with the dynamic and multifaceted nature of the atmosphere such as meteorological variability, local emissions and sources, data quality and availability, and emerging pollutants. Extensive experiments prove that the search tree approach to find Eps is quicker and efficient in comparison to the widely used KNN algorithm. The time reduction to find Eps makes a significant impact as the dataset size increases. The input parameters are fed to DBSCAN algorithm to obtain clustering results.

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“…DBSCAN takes in 2 input parameters- [90], [91] where ε represents the radius of the circle formed with data object as centre and minpts() represents the number of points inside the circle. The DBSCAN starts by determining the surroundings starting from an unexplored, random starting point.…”

Section: Density-based Spatial Clustering Of Applications With Noise(...mentioning

confidence: 99%

PAACDA: Comprehensive Data Corruption Detection Algorithm

et al. 2023

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With the advent of technology, data and its analysis are no longer just values and attributes strewn across spreadsheets, they are now seen as a stepping stone to bring about revolution in any significant field. Data corruption can be brought about by a variety of unethical and illegal sources, making it crucial to develop a method that is highly effective to identify and appropriately highlight the various corrupted data existing in the dataset. Detection of corrupted data, as well as recovering data from a corrupted dataset, is a challenging problem. This requires utmost importance and if not addressed at earlier stages may pose problems in later stages of data processing with machine or deep learning algorithms. In the following work we begin by introducing the PAACDA: Proximity based Adamic Adar Corruption Detection Algorithm and consolidating the results whilst particularly accentuating the detection of corrupted data rather than outliers. Current state of the art models, such as Isolation forest, DBSCAN also called "Density-Based Spatial Clustering of Applications with Noise" and others, are reliant on fine-tuning parameters to provide high accuracy and recall, but they also have a significant level of uncertainty when factoring the corrupted data. In the present work, the authors look into the most niche performance issues of several unsupervised learning algorithms for linear and clustered corrupted datasets. Also, a novel PAACDA algorithm is proposed which outperforms other unsupervised learning benchmarks on 15 popular baselines including K-means clustering, Isolation forest and LOF (Local Outlier Factor) with an accuracy of 96.35% for clustered data and 99.04% for linear data. This article also conducts a thorough exploration of the relevant literature from the previously stated perspectives. In this research work, we pinpoint all the shortcomings of the present techniques and draw direction for future work in this field.

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Automated Determination of the Input Parameter of DBSCAN Based on Outlier Detection

Cited by 15 publications

References 13 publications

First Arrival Picking of Zero-Phase Seismic Data by Hilbert Envelope Empirical Half Window (HEEH) Method

First Arrival Picking of Zero-Phase Seismic Data by Hilbert Envelope Empirical Half Window (HEEH) Method

A Machine Learning Approach for Environmental Assessment on Air Quality and Mitigation Strategy

PAACDA: Comprehensive Data Corruption Detection Algorithm

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