Improvement of K-means Cluster Quality by Post Processing Resulted Clusters

Borlea, Ioan-Daniel; Precup, Radu; Borlea, Alexandra-Bianca

doi:10.1016/j.procs.2022.01.009

Cited by 82 publications

(30 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, the clustering centers of each sample are calculated through the message transmission of each edge in the matrix. Compared to the widely used K-means clustering algorithm [15,16] , the advantage of the AP algorithm is that it does not need to specify parameters that describe the number of clusters (such as the K-parameter in the K-means clustering algorithm), which makes prior experience a non-essential condition for algorithm application and effectively improves the applicability of the clustering algorithm.…”

Section: Corridor Strip Search Methods Based On Nnia and Ap Clusterin...mentioning

confidence: 99%

Corridor selection method of new highway based on NNIA genetic algorithm

Wu,

Zhu,

Wang

et al. 2024

Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023)

View full text Add to dashboard Cite

The selection of the highway corridor is very significant in the early stage of highway construction, and its rationality determines the economy, environmental protection, and safety of the whole life cycle of the construction project. Existing corridor selection methods are mainly based on the subjective experience of designers and lack intelligent corridor selection technology with strong operability. Therefore, a corridor selection method for new highways based on Nondominated Neighbor Immune Algorithm (NNIA) and Affinity Propagation (AP) clustering algorithm was proposed. Firstly, the mathematical model of the corridor was built. Secondly, considering the environmental and economic factors during the corridor selection stage, the optimization objective function was established. Lastly, the corridor was generated by using the NNIA and AP clustering algorithms. This method was applied to the project from Dezhou to Shangrao Highway from Qimen to the boundary of Anhui and Jiangxi provinces. The corridor generated was consistent with the trend of the actual route of the project, which had certain engineering feasibility.

show abstract

Section: Corridor Strip Search Methods Based On Nnia and Ap Clusterin...mentioning

confidence: 99%

Corridor selection method of new highway based on NNIA genetic algorithm

Wu,

Zhu,

Wang

et al. 2024

Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023)

View full text Add to dashboard Cite

show abstract

“…As a result, the proposed 3D descriptor performs well when used with 3D datasets like those from the Kinect for 3D object detection. Borlea et al [21] this paper presents a way of improving the resulted clusters generated by the K-means algorithm by postprocessing the resulted clusters with a supervised learning algorithm. Te proposed approach is focused on improving the quality of the resulting clusters and not on reducing the processing time.…”

Section: Related Workmentioning

confidence: 99%

Oppositional Cat Swarm Optimization‐Based Feature Selection Approach for Credit Card Fraud Detection

Prabhakaran

Nedunchelian

2023

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Credit card fraud has drastically increased in recent times due to the advancements in e-commerce systems and communication technology. Falsified credit card transactions affect the financial status of the companies as well as clients regularly and fraudsters incessantly try to develop new approaches to commit frauds. The recognition of credit card fraud is essential to sustain the trustworthiness of e-payments. Therefore, it is highly needed to design effective and accurate credit card fraud detection (CCFD) techniques. The recently developed machine learning (ML) and deep learning (DL) can be employed for CCFD because of the characteristics of building an effective model to identify fraudulent transactions. In this view, this study presents a novel oppositional cat swarm optimization-based feature selection model with a deep learning model for CCFD, called the OCSODL-CCFD technique. The major intention of the OCSODL-CCFD technique is to detect and classify fraudulent transactions using credit cards. The OCSODL-CCFD technique derives a new OCSO-based feature selection algorithm to choose an optimal subset of features. Besides, the chaotic krill herd algorithm (CKHA) with the bidirectional gated recurrent unit (BiGRU) model is applied for the classification of credit card frauds, in which the hyperparameter tuning of the BiGRU model is performed using the CKHA. To demonstrate the supreme outcomes of the OCSODL-CCFD model, a wide range of simulation analyses were carried out. The extensive comparative analysis highlighted the better outcomes of the OCSODL-CCFD model over the compared ones based on several evaluation metrics.

show abstract

“…Consequently,

is more suitable for identifying the consumer value of “Internet + Recycling”. Based on

model, k-means algorithm [ 21 ] is used to obtain the cluster centers.…”

Section: Acquisition Of Customer Cluster Centersmentioning

confidence: 99%

Sorting Center Value Identification of “Internet + Recycling” Based on Transfer Clustering

Cheng

Luan

2022

Sensors

View full text Add to dashboard Cite

As the core link of the “Internet + Recycling” process, the value identification of the sorting center is a great challenge due to its small and imbalanced data set. This paper utilizes transfer fuzzy c-means to improve the value assessment accuracy of the sorting center by transferring the knowledge of customers clustering. To ensure the transfer effect, an inter-class balanced data selection method is proposed to select a balanced and more qualified subset of the source domain. Furthermore, an improved RFM (Recency, Frequency, and Monetary) model, named GFMR (Gap, Frequency, Monetary, and Repeat), has been presented to attain a more reasonable attribute description for sorting centers and consumers. The application in the field of electronic waste recycling shows the effectiveness and advantages of the proposed method.

show abstract

Improvement of K-means Cluster Quality by Post Processing Resulted Clusters

Cited by 82 publications

References 11 publications

Corridor selection method of new highway based on NNIA genetic algorithm

Corridor selection method of new highway based on NNIA genetic algorithm

Oppositional Cat Swarm Optimization‐Based Feature Selection Approach for Credit Card Fraud Detection

Sorting Center Value Identification of “Internet + Recycling” Based on Transfer Clustering

Contact Info

Product

Resources

About