Clustering of Female Avatar Face Features Consumers Choice using KMeans and SOM Algorithm

Megawati, Citra Dewi; Yuniarno, Eko Mulyanto; Nugroho, Supeno Mardi Susiki

doi:10.1109/isitia.2019.8937279

Cited by 4 publications

(1 citation statement)

References 5 publications

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“…Many applications need a fast method to solve the clustering problems. Currently many methods have been used as a clustering method such as DBSCAN, K-Means [13], Mean Shift [14], and OPTICS [15]. The DBSCAN method is believed to be the fastest method of all those methods and it able to clustering data while reducing the existing noise [16].…”

Section: Introductionmentioning

confidence: 99%

DBSCAN for Hand Tracking and Gesture Recognition

Aditya

Tolle

Shih

2020

JITeCS

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Hand segmentation and tracking are important issues for hand-gesture recognition. Using depth data, it can speed up the segmentation process because we can delete unnecessary data like the background of the image easily. In this research, we modify DBSCAN clustering algorithm to make it faster and suitable for our system. This method is used in both hand tracking and hand gesture recognition. The results show that our method performs well in this system. The proposed method can outperform the original DBSCAN and the other clustering method in terms of computational time.

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Section: Introductionmentioning

confidence: 99%

DBSCAN for Hand Tracking and Gesture Recognition

Aditya

Tolle

Shih

2020

JITeCS

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Electric vehicle charging current scenario generation based on generative adversarial network combined with clustering algorithm

Yang

Yin

Zhou

et al. 2021

Int Trans Electr Energ Syst

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Summary The generation of charging current scenario is an important step in the operation and planning of power systems with high electric vehicle (EV) penetrations. With the development of the modeling method, a number of methods based on probabilistic models are applied to generate scenarios. Model‐based methods are often difficult to scale or sample. Data‐driven technologies use a large number of data to mine the mapping relationships, instead of explicitly specifying a model. In this paper, we proposed a data‐driven approach to generate scenarios using generative adversarial networks (GANs), which can learn the distribution of the charging current of EVs and obtain more abundant scenarios. The proposed method is applied to time‐series data from the charging current dataset of EVs. Firstly, the K‐Means clustering algorithm is used to preprocess the data to divide the distribution of charging current into four areas. Then, aiming to improve the training speed, GANs with gradient penalty (GP) is used for the generation of EV scenarios, which can use the GP term to optimize the Lipschitz limit. Finally, statistical methods are applied to estimate the quality of the generated data. Results show that the proposed method can effectively extend the historical data for the operation and planning of EVs in the future compared with the traditional GANs.

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