μDBSCAN: An Exact Scalable DBSCAN Algorithm for Big Data Exploiting Spatial Locality

Sarma, Aditya; Goyal, Poonam; Kumari, Sonal; Wani, Anand; Challa, Jagat Sesh; Islam, Saiful; Goyal, Navneet

doi:10.1109/cluster.2019.8891020

Cited by 22 publications

(10 citation statements)

References 27 publications

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“…This finding indicates that the time complexity of the method is reduced to O( n ) (detailed derivation is explained in the experimental section), suggesting a higher efficiency than the representative software‐based DBSCAN algorithms, as shown in Figure 6c. [ 8,33–38 ]…”

Section: Resultsmentioning

confidence: 99%

Parallel Density‐Based Spatial Clustering with Dual‐Functional Memristive Crossbar Array

Cheong,

Shin,

Lee

et al. 2023

Adv Funct Materials

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Analog and digital switching performances in a Ta/HfO2/RuO2 (THR) memristor are studied to implement a density‐based spatial clustering of applications with noise (DBSCAN) algorithm in a low‐power, parallel‐computing memristor crossbar structure. In the analog mode THR memristor, more than 256 states can be stored through a fine‐tuning process with a denoising scheme. The analog mode crossbar array facilitates Euclidean distance calculation between any points in the given graphic dataset. In the digital mode, the on/off ratio of more than three orders of magnitude between the binary states is achieved, providing functionality to cluster the data points with a reduced number of operations. The parallel computing capacity of the adopted crossbar decreases the time complexity of the original DBSCAN from O(n2) to O(n). Through array‐level simulations, the effectiveness of hardware functionality is validated using representative synthetic datasets and single‐cell RNA sequences datasets.

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Section: Resultsmentioning

confidence: 99%

Parallel Density‐Based Spatial Clustering with Dual‐Functional Memristive Crossbar Array

Cheong,

Shin,

Lee

et al. 2023

Adv Funct Materials

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“…The second test was carried out with ‘Spiral’ dataset [32]. ‘Spiral’ has 312 two‐dimensional data belonging to three pre‐defined clusters.…”

Section: Methods Verificationsmentioning

confidence: 99%

“…For two‐dimensional datasets, the time complexity of the classical DBSCAN is O ( n 2 ) [32]. The time complexity of the proposed parameter adaptive setting method comes from: (1) calculate Euclidean distance between any two data in the dataset with ‘for’ loops, and (2) calculate local densities of every data with ‘for’ loops.…”

Section: Dbscan With Proposed Parameter Values Adaptive Setting Methodsmentioning

confidence: 99%

A practical building energy consumption anomaly detection method based on parameter adaptive setting DBSCAN

Yao

Chang

et al. 2021

Cognitive Comp and Systems

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In order to realize the Building Energy Consumption Anomaly Detection (BECAD) for the green building assessment, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is adopted for data clustering. To deal with the parameter setting difficulty of the DBSCAN, a practical parameter adaptive setting method is proposed. The presented method determines values of the DBSCAN parameters, MinPts and ε, according to four distribution characteristics (average data distance, data local densities, cosine similarity, and equivalent space radius) of data, and does not need prior knowledge of the datasets. Furthermore, parameter values determined by the proposed method can improve the clustering effect of the DBSCAN on datasets with various data densities. After testing the proposed method with open datasets, DBSCAN with the parameter adaptive setting method is applied to the BECAD. Experiment results show that identified building energy utilization patterns and abnormal buildings are reasonable and the results can offer the management departments a clear understanding of building energy consumption patterns, as well as decision supports to make subsequent improvement measures.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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“…Cloud computing solves the big data problem, reducing energy consumption in network industrial sensors, improving security, processing and real-time data storage. In addition to solving major limitations in processing large databases, algorithms with low computational complexity (CC) have been developed (Arnaiz-González et al 2016;Baldán & Benítez 2019;Sarma et al 2019;Baldán et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Predicting time-series for water demand in the big data environment using statistical methods, machine learning and the novel analog methodology dynamic time scan forecasting

2023

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The specialized literature on water demand forecasting indicates that successful predicting models are based on soft computing approaches such as neural networks, fuzzy systems, evolutionary computing, support vector machines and hybrid models. However, soft computing models are extremely sensitive to sample size, with limitations for modeling extensive time-series. As an alternative, this work proposes the use of the dynamic time scan forecasting (DTSF) method to predict time-series for water demand in urban supply systems. Such a model scans a time-series looking for patterns similar to the values observed most recently. The values that precede the selected patterns are used to create the prediction using similarity functions. Compared to soft computing approaches, the DTSF method has very low computational complexity and is indicated for large time-series. Results presented here demonstrate that the proposed method provides similar or improved forecast values, compared to soft computing and statistical methods, but with lower computational cost. Thus, its use for online water demand forecasts is favored.

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μDBSCAN: An Exact Scalable DBSCAN Algorithm for Big Data Exploiting Spatial Locality

Cited by 22 publications

References 27 publications

Parallel Density‐Based Spatial Clustering with Dual‐Functional Memristive Crossbar Array

Parallel Density‐Based Spatial Clustering with Dual‐Functional Memristive Crossbar Array

A practical building energy consumption anomaly detection method based on parameter adaptive setting DBSCAN

Predicting time-series for water demand in the big data environment using statistical methods, machine learning and the novel analog methodology dynamic time scan forecasting

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