An intelligent clustering algorithm for high-dimensional multiview data in big data applications

Qian, Tao; Gu, Chunqin; Wang, Zhenyu; Jiang, Daoning

doi:10.1016/j.neucom.2018.12.093

Cited by 19 publications

(6 citation statements)

References 36 publications

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“…Therefore, high-dimensional clustering of incomplete mixed data sets in this paper is of great significance, and some related literature has achieved some results. To tackle this challenging problem, [50] proposes a novel intelligent weighting k-means clustering (IWKM) algorithm based on swarm intelligence. Finally verify the clustering performance of high-dimensional multi-view data.…”

Section: Discussionmentioning

confidence: 99%

High-Dimensional Clustering for Incomplete Mixed Dataset Using Artificial Intelligence

2020

IEEE Access

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In order to address the problem that high energy consumption, high memory usage and low clustering effect in traditional data set high-dimensional clustering algorithms, we propose the highdimensional clustering algorithm of incomplete mixed data set based on artificial intelligence. First, we construct the phase space reconstruction to ensure the invariance of features of incomplete mixed data set by analyzing the incomplete mixed data set and introduce the correlation dimension to obtain the feature correlation value. Second, we introduce the standard deviation and realize the extraction of features of incomplete mixed data set through calculating the sparsity of sample features. Third, we conduct repeated clustering for the mixed data set in the subspace according to the degree of correlation between incomplete mixed data sets in the multidimensional subspace. Last, we realize the design of high-dimensional clustering method for incomplete mixed data set in accordance with the stronger relevance in the mixed data sets. Experimental results show that the proposed algorithm has good correlation dimension processing effects, lower memory usage, time-consuming, lower and concentrated ensemble energy consumption (within 300J), good clustering effects, as high as 92%, which has some advantages and practical application value.

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Section: Discussionmentioning

confidence: 99%

High-Dimensional Clustering for Incomplete Mixed Dataset Using Artificial Intelligence

2020

IEEE Access

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“…Simonetto et al propose a memetic algorithm to optimize the number of times the unstructured balanced symbolic network evolves to the structured balanced dynamic [11]. Since, in practice, the network connection relation weights are difficult to measure accurately and it is generally impossible to control them directly, only a very few pieces of literature have explored the characteristics of structural equilibrium through the role of control to make the population of connection relations emerge [12,13].…”

Section: Related Workmentioning

confidence: 99%

Adaptive Model of Discrete Real-Time Linear Dynamic Logic for the Optimization of Big Data Game Education Mode

Liang¹,

Zhang²

2022

Scientific Programming

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With the advent of the era of big data and mobile Internet, the traditional teaching model of colleges and universities has been hit unprecedentedly. In the current big data and artificial intelligence network environment, new teaching concepts, characteristics, and model applications should be analyzed, and an education model based on artificial intelligence adaptive control and negative feedback adjustment mechanisms under the big data environment should be explored. The study of this model can provide practical experience and reference value for the application, popularization, and innovation of teaching information technology in the future. Combining the characteristics of big data games, this paper proposes a college student education model based on discrete dynamic model big data game. The scores of college students represent comprehensive information such as their qualifications to provide big data university education, the credit of historical records, and the degree of reliable data transmission. Based on the general model of the adaptive learning system, we have perfected the functions of each module in the overall structure of the domain-based special subject system and designed the overall workflow of the system. Computerized evaluation is introduced into the learner module, and an adaptive evaluation system structure based on cognitive diagnosis theory is designed to achieve a personalized topic selection model for different learners and enrich learners’ experience through more detailed and specialized evaluation results. The model lays the foundation for adaptive course path recommendation. This paper proposes a discrete real-time linear dynamic logic that can express discrete real-time characteristics and all regular characteristics. It can easily describe the behavior of some complex real-time systems. Experiments show that the discrete dynamic model big data game efficiency is improved by 35.71%, which can better optimize the education model of college students. In addition, it can be concluded that the research on the effectiveness of college student education in the era of big data not only determines the safety of the ideological field of colleges and universities but also affects the thinking orientation of college students.

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“…To deal with this issue, the authors proposed an intelligent weighted k-means clustering technique for multiview data with high-dimensional in various big data applications. The authors formed a distance function based on various weights of views and features for determining object clusters [34]. Moreover, global search is employed by the PSO algorithm for the elimination of sensitivity of initially selected cluster centres.…”

Section: Literature Reviewmentioning

confidence: 99%

Boreholes Data Analysis Architecture Based on Clustering and Prediction Models for Enhancing Underground Safety Verification

et al. 2021

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During the last decade, substantial resources have been invested to exploit massive amounts of boreholes data collected through groundwater extraction. Furthermore, boreholes depth can be considered one of the crucial factors in digging borehole efficiency. Therefore, a new solution is needed to process and analyze boreholes data to monitor digging operations and identify the boreholes shortcomings. This research study presents a boreholes data analysis architecture based on data and predictive analysis models to improve borehole efficiency, underground safety verification, and risk evaluation. The proposed architecture aims to process and analyze borehole data based on different hydrogeological characteristics using data and predictive analytics to enhance underground safety verification and planning of borehole resources. The proposed architecture is developed based on two modules; descriptive data analysis and predictive analysis modules. The descriptive analysis aims to utilize data and clustering analysis techniques to process and extract hidden hydrogeological characteristics from borehole history data. The predictive analysis aims to develop a bi-directional long short-term memory (BD-LSTM) to predict the boreholes depth to minimize the cost and time of the digging operations. Furthermore, different performance measures are utilized to evaluate the performance of the proposed clustering and regression models. Moreover, our proposed BD-LSTM model is evaluated and compared with conventional machine learning (ML) regression models. The R 2 score of the proposed BD-LSTM is 0.989, which indicates that the proposed model accurately and precisely predicts boreholes depth compared to the conventional regression models. The experimental and comparative analysis results reveal the significance and effectiveness of the proposed borehole data analysis architecture. The experimental results will improve underground safety management and the efficiency of boreholes for future wells.

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An intelligent clustering algorithm for high-dimensional multiview data in big data applications

Cited by 19 publications

References 36 publications

High-Dimensional Clustering for Incomplete Mixed Dataset Using Artificial Intelligence

High-Dimensional Clustering for Incomplete Mixed Dataset Using Artificial Intelligence

Adaptive Model of Discrete Real-Time Linear Dynamic Logic for the Optimization of Big Data Game Education Mode

Boreholes Data Analysis Architecture Based on Clustering and Prediction Models for Enhancing Underground Safety Verification

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