Automatic fuzzy-DBSCAN algorithm for morphological and overlapping datasets

Yelghi, Aref; Köse, Cemal; Yelghi, Asef; Shahkar, Amir

doi:10.23919/jsee.2020.000095

Cited by 14 publications

(8 citation statements)

References 13 publications

(16 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, these connections may serve as forecasters of future events. Data mining's significance has been demonstrated for commercial applications, criminal investigations, biomedicine [2], and more recently, counter-terrorism [3][4][5]. For example, most merchants utilize data mining techniques to discover consumer purchasing trends.…”

Section: Introductionmentioning

confidence: 99%

“…It employs the EM methodology where the E-step assigns the data points to the closest clusters, while the M-step is the computation process of the centroid for each set. Using the k-means cluster analysis aims to organize psychologically relevant parameters of expectations based on individual elements into statistically homogenous cluster groupings [2].…”

Section: Introductionmentioning

confidence: 99%

“…Objects in the same group become insignificant, but differences across clusters are noteworthy. Identifying the first cluster centers is necessary to discover minor discrepancies between the indicators under consideration in one group [2,3]. A study by [6][7][8][9][10][11][12] indicated a k-means clustering algorithm's merits over density-based clustering.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Research on the Application of Data Mining Technology in the Analysis of College Students’ Sports Psychology

Hou

2021

Mobile Information Systems

View full text Add to dashboard Cite

The advent of the information age has changed every existing career and revolutionized most if not all fields, notwithstanding many benefits that came along with it. There has been an exponential rise in information and, alongside it, an increase in data. Data centers have erupted with details as the number of rows in databases grows by the day. The use of technology has nevertheless become essential in many company models and organizations, warranting its usage in virtually every channel. College physical education and sports are not an exception as students studying such subjects are skyrocketing. As the information is getting more complex, improved methods are needed to research and analyze data. Fortunately, data mining has come to the rescue. Data mining is a collection of analytical methods and procedures used exclusively for the sake of data extraction. It may be used to analyze features and trends from vast quantities of data. The objective of this study is to explore the use of data mining technologies in the analysis of college students’ sports psychology. This study uses clustering methods for the examination of sports psychology. We utilize three clustering methods for this aim: expectation-maximization (EM) algorithm, k-means, COBWEB, density-based clustering of applications with noise (DBSCAN), and agglomerative hierarchal clustering algorithms. We perform our forecasts based on various metrics combined with the past outcomes of college sports using these methods. In contrast to conventional data research and analysis techniques, our approaches have relatively high prediction accuracy as far as college athletics is concerned.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Research on the Application of Data Mining Technology in the Analysis of College Students’ Sports Psychology

Hou

2021

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…Among the clustering algorithms, the K-means clustering algorithm is simple, easy to implement, and converges quickly, but it has high requirements for the selection of initial clustering points and may fall into a locally optimal solution [6] . The density-based spatial clustering of applications with noise (DBSCAN) algorithm [27] does not need to determine the cluster center and number in advance, but it needs to determine the density threshold first. The clustering effect is not good for samples with uneven density and large spacing.…”

Section: Density Clustering Algorithmmentioning

confidence: 99%

Keyframe Extraction and Process Recognition Method for Assembly Operation Based on Density Clustering

et al. 2023

View full text Add to dashboard Cite

A keyframe extraction and process recognition method for assembly operation is proposed based on density clustering to solve the problems of data redundancy and difficulty in obtaining valid data frames from the process of continuous assembly operation. A standard operation gesture set including dynamic and static action was constructed through assembly operation decomposing. Finger feature variables and gesture comprehensive feature quantized function were defined according to finger joint structure. Based on searching for local extreme points of the function, density clustering method was used to extract the keyframes of the assembly operation sequence to eliminate the redundant data. Finally, the support vector machine algorithm model and the Levinstein distance were determined to complete the keyframe recognition and assembly operation matching. A case study showed that the method presented can effectively discretize the assembly operation sequence, remove approximately 84% of redundant data frames, and achieve a comprehensive recognition rate of 92%.

show abstract

“…Traditionally, researchers mainly focused on the unsupervised machine learning algorithms, such as K-means (Sinaga and Yang, 2020), FCM (fuzzy C-means) (Sun et al, 2019a), and DBSCAN (density-based spatial clustering of applications with noise) (Aref et al, 2020) algorithms. Peng et al (2014) identified the patterns of the power load using K-means, K-medoids, SOM (self-organizing maps), and FCM.…”

Section: Introductionmentioning

confidence: 99%

An improved deep learning algorithm in enabling load data classification for power system

Ziyao

Li²,

Wu³

2022

Front. Energy Res.

View full text Add to dashboard Cite

Load behaviors significantly impact the planning, dispatching, and operation of the modern power systems. Load classification has been proved as one of the most effective ways of analyzing the load behaviors. However, due to the issues of data collection, transmission, and storage in current power systems, data missing problems frequently occur, which prevents the load classification tasks from precisely identifying the load classes. Simultaneously, because of the diversities of the load categories, different loads contribute various amounts of data, which causes the class imbalance issue. The traditional load data classification algorithms lack the ability to solve the aforementioned issues, which may deteriorate the load classification accuracy. Therefore, this study proposed an improved deep learning algorithm based on the load classification approach in terms of raising the classification performances with solving the data missing and class imbalance issues. First, the LATC (low-rank autoregressive tensor completion) algorithm is used to solve the data missing issue to improve the quality of the training dataset. A Borderline-SMOTE algorithm is further adopted to improve the class distribution in the training dataset to improve the training performances of biGRU (bidirectional gated recurrent unit). Afterward, to improve the classification accuracy in the classification task, the biGRU algorithm, combined with the attention mechanism, is used as the underlying infrastructure. The experimental results show the effectiveness of the proposed approach.

show abstract

Automatic fuzzy-DBSCAN algorithm for morphological and overlapping datasets

Cited by 14 publications

References 13 publications

Research on the Application of Data Mining Technology in the Analysis of College Students’ Sports Psychology

Research on the Application of Data Mining Technology in the Analysis of College Students’ Sports Psychology

Keyframe Extraction and Process Recognition Method for Assembly Operation Based on Density Clustering

An improved deep learning algorithm in enabling load data classification for power system

Contact Info

Product

Resources

About