Application Research of KNN Algorithm Based on Clustering in Big Data Talent Demand Information Classification

Xiao, Qing-Tao; Zhong, Xin; Zhong, Chenghua

doi:10.1142/s0218001420500159

Cited by 19 publications

(6 citation statements)

References 18 publications

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“…This is the second difficulty. Judging from the published images after the launch of the EO-1 satellite, the quality is good, but the quantitative evaluation of its geometry and radiation characteristics needs to be verified after verification [ 16 , 17 ]. Based on the CHRIS multiangle hyperspectral remote sensing data and the understanding of the investigation of the test area, using multiangle hyperspectral remote sensing data, mainly centering on the subject of forest type classification, the following key work has been progressed.…”

Section: Methodsmentioning

confidence: 99%

Multimedia Image Data Analysis Based on KNN Algorithm

2022

Computational Intelligence and Neuroscience

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In order to improve the authenticity of multispectral remote sensing image data analysis, the KNN algorithm and hyperspectral remote sensing technology are used to organically combine advanced multimedia technology with spectral technology to subdivide the spectrum. Different classification methods are used to classify CHRIS 0°, and the results are analyzed and compared: SVM classification accuracy is the highest 72 8448%, Kappa coefficient is 0.6770, and SVM is used to classify CHRIS images from five angles, and the results are compared and analyzed: the classification accuracy is from high to low, and the order is FZA = 0 > FZA = −36 > FZA = −55 > FZA = 36 > FZA = 55; SVM is used to classify the multiangle combined image, and the result is compared with the CHRIS 0° result: the overall classification accuracy of angle-combined image types is lower than that of single-angle images; the SVM is used to classify the band-combined image, and the result is compared with CHRIS 0°: the overall classification accuracy of the band combination image forest type is very low, and the effect is not as good as the combining multiangle image classification results. It is verified that if CHRIS multiangle hyper-spectral data are used for classification, the SVM method should be used to classify spectral remote sensing image data with the best effect.

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Section: Methodsmentioning

confidence: 99%

Multimedia Image Data Analysis Based on KNN Algorithm

2022

Computational Intelligence and Neuroscience

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“…Text clustering is a common method in text mining technology and is widely used in many fields, including topic discovery and hotspot tracking. Some studies have extracted high-quality information through data processing and further refined concepts related to the occupation in question using LDA [15], [26], Word2vec [27], [28], BERT [28], and TF-IDF [29] to understand the dynamics of the labor market.…”

Section: Big Data Job Market Trendsmentioning

confidence: 99%

Analyzing Big Data Professionals: Cultivating Holistic Skills Through University Education and Market Demands

Han,

Ren

2024

IEEE Access

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This study investigates the alignment between the education of big data professionals in universities and the requirements of the labor market. Through the examination of data extracted from Chinese job advertisements, this study explores the multifaceted skill set sought by employers in the big data domain, with an emphasis on achieving a harmonious equilibrium between technical ("hard") and soft skills. Employers expect a high level of proficiency in data processing and analysis coupled with capabilities in teamwork, communication, and leadership. The dynamic nature of big data, characterized by rapid technological advancements, underscores the importance of continuous learning. The demand for big data talent has various roles, including big data scientists, engineers, and workers, thus contributing to a diverse and competitive job market. Recognizing the potential gap between university education and the dynamic requirements of the job market, this study advocates the integration of practical skills with theoretical knowledge. It proposes that universities assert their autonomy in curriculum design while concurrently engaging with alumni and industry partners to afford students' real-world experiences. In conclusion, this study underscores the significance of cultivating a sustainable talent development system. This encourages a curriculum that adeptly balances timeliness and innovation, producing graduates armed with contemporary tools and a comprehensive understanding of data science principles. This approach fosters versatile professionals capable of effectively addressing the myriad challenges within the diverse landscapes of the industry.

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“…K Nearest Neighbors (KNN) algorithm is based on the idea of proximity, where each new data point is classified by analyzing its k closest neighbours and assigning it to the class that is most frequently represented among them [63].…”

Section: ) Random Forest (Rf)mentioning

confidence: 99%

Dynamics of Digital Pen-Tablet: Handwriting Analysis for Person Identification Using Machine and Deep Learning Techniques

Hasan,

Rahim,

Shin

et al. 2024

IEEE Access

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Handwriting is controlled by neurons in the brain's nervous system, reflecting an individual's personality and psychology. This unique characteristic can be used for various applications, including user authentication, assessment of neurodegenerative disorders, and classification of handedness, gender, and age groups. Traditional authentication systems require memorization, information leakage, and fingerprints, making them vulnerable to security breaches. The majority of researchers have studied the limitations of image quality, camera frames, and light effects on text and image-dependent performance. Therefore, this paper mainly focused on real-time, text-independent handwriting fine-motor data and proposed an efficient authentication system with low cost using efficient feature extraction and optimal feature selection approaches. This research utilizes two benchmark databases, including the handwriting data of 48 (24+24) participants collected via a sensor-based pen tablet. Each participant wrote the 10 words five times repeatedly, making it a total of 2400 samples. The handwriting classification of the different individuals is in 3 phases: feature extraction, feature selection, and classification. A total of 91 features (statistical, kinematic, spatial, and composite) were extracted from more accurate, real-time numerical handwriting data. The efficient and optimal features have been selected using four feature selection approaches, namely, Pearson's r correlation, ANOVA-F, Mutual Information Gain, and PCA, among which the ANOVA-F test and PCA perform well for handwriting-extracted data. Then, 14 machine learning (ML) models and 7 deep learning (DL) models were applied to handle the problem of individual classification, with both no-and full-feature-selection scenarios considered. The experimental analysis has been conducted with different angles and perspectives, such as K-Fold cross-validation, testing system efficiency considering 5/10/15/24/48 individuals, and in the case of individual tasks. It shows that ML-based algorithms, namely, CATBOOST (99.07%) with ANOVA-F and DL-based models, namely, BiLSTM (98.31%) with PCA-selected features, provide the highest accuracy with dataset 2, among others that advocate the practicality and reliability of choosing this system for user identification.

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Application Research of KNN Algorithm Based on Clustering in Big Data Talent Demand Information Classification

Cited by 19 publications

References 18 publications

Multimedia Image Data Analysis Based on KNN Algorithm

Multimedia Image Data Analysis Based on KNN Algorithm

Analyzing Big Data Professionals: Cultivating Holistic Skills Through University Education and Market Demands

Dynamics of Digital Pen-Tablet: Handwriting Analysis for Person Identification Using Machine and Deep Learning Techniques

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