Automatic Text Summarization Method Based on Improved TextRank Algorithm and K-Means Clustering

Liu, Wenjun; Sun, Yuyan; Yu, Bao; Wang, Hailan; Peng, Qingcheng; Hou, Mengshu; Guo, Huan; Wang, Hai; Liu, Cheng

doi:10.1016/j.knosys.2024.111447

Cited by 5 publications

(2 citation statements)

References 25 publications

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“…In short, the goal of clustering is to divide the dataset into multiple categories according to some criteria (such as the closest distance between elements, the farthest distance, or the average distance), so that the characteristics of data points within the same category are as consistent as possible, while the data points between different categories show greater differences. For example, K-means [11], density clustering [12][13][14], hierarchical clustering [15,16], spectral clustering [17][18][19], and incremental clustering [20][21][22] can effectively classify wind turbines to optimize operation and maintenance strategies and improve energy output efficiency. ST-TRACLUS was proposed in reference [23], which is a novel spatio-temporal clustering algorithm, which enhances the DBSCAN framework through spatial and temporal analysis to identify similarities in trajectory data.…”

Section: Related Workmentioning

confidence: 99%

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Research on the Short-Term Prediction of Offshore Wind Power Based on Unit Classification

Zhang,

Liu,

Yan

2024

Electronics

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The traditional power prediction methods cannot fully take into account the differences and similarities between units. In the face of the complex and changeable sea climate, the strong coupling effect of atmospheric circulation, ocean current movement, and wave fluctuation, the characteristics of wind processes under different incoming currents and different weather are very different, and the spatio-temporal correlation law of offshore wind processes is highly complex, which leads to traditional power prediction not being able to accurately predict the short-term power of offshore wind farms. Therefore, aiming at the characteristics and complexity of offshore wind power, this paper proposes an innovative short-term power prediction method for offshore wind farms based on a Gaussian mixture model (GMM). This method considers the correlation between units according to the characteristics of the measured data of units, and it divides units with high correlation into a category. The Bayesian information criterion (BIC) and contour coefficient method (SC) were used to obtain the optimal number of groups. The average intra-group correlation coefficient (AICC) was used to evaluate the reliability of measurements for the same quantized feature to select the representative units for each classification. Practical examples show that the short-term power prediction accuracy of the model after unit classification is 2.12% and 1.1% higher than that without group processing, and the mean square error and average absolute error of the short-term power prediction accuracy are reduced, respectively, which provides a basis for the optimization of prediction accuracy and economic operation of offshore wind farms.

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Section: Related Workmentioning

confidence: 99%

“…K-means is a simple and efficient unsupervised learning algorithm [11] known for its simple structure and easy operation. It is widely used in clustering analysis, and is favored by researchers and data scientists because of its ability to quickly process large datasets.…”

Section: The K-means Modelmentioning

confidence: 99%

Research on the Short-Term Prediction of Offshore Wind Power Based on Unit Classification

Zhang,

Liu,

Yan

2024

Electronics

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An Accuracy Study of Personalized Recommendation System for E-commerce Based on Big Data Analysis

Zhang

2024

Applied Mathematics and Nonlinear Sciences

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E-commerce, as an emerging value chain model for the global economy, has greatly promoted development, while the impact of digitalization on traditional publishing enterprises is increasingly evident. In this paper, we propose a TextRank keyword extraction algorithm based on comprehensive weights, which extracts and assigns keywords that identify user information, behavior, and product characteristics. We then output a keyword weight table for user information, user behavior, and product keywords. Finally, utilizing an optimized collaborative filtering recommendation algorithm, we establish a recommendation model between the user-commodity matrix to build an e-commerce personalized recommendation system that provides users with more accurate customized recommendations. The practical application of the designed personalized recommendation system is examined to evaluate its accuracy. The MAE of this algorithm is smaller than that of user-based (0.8915, 0.9470) or item-based (0.8873, 0.9327) collaborative filtering algorithms, indicating that the improved collaborative filtering algorithm effectively enhances system recommendation accuracy. The direct effect value of recommendation strength is 0.344, with an indirect effect value of 0.018, leading to the highest overall effect value. This study provides users with convenient and attentive services, significantly enhances user experience quality, and generates substantial profits for the e-commerce platform.

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Innovative Cultural Tourism in the Perspective of Cultural Ecology in the Context of Big Data: Theory, Strategy, Practice

Chu

2024

Applied Mathematics and Nonlinear Sciences

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Taking cultural ecology as the carrier of tourism resources, it fully embodies the significance of the synergistic development of cultural tourism ecology for the innovation of the cultural tourism model. The article establishes a cultural tourism ecological synergy model by integrating cultural ecology and cultural tourism under the guidance of the theory of the life cycle of tourist places and industrial innovation. Taking the nine provinces of the Yellow River as the research object, its cultural tourism data are collected and pre-processed, cultural tourism keywords are extracted by the TextRank algorithm, and the LDA model is established to obtain cultural tourism themes. The Skip-gram model was combined with the GCN network to establish a cultural tourism emotional image perception model, and the Moran index and nearest-neighbor index were introduced to analyze the spatial structure and regional evolution of cultural tourism attractions. The keywords of cultural tourism are mainly embodied in ecology and culture, which all appear more than 1,000 times, and the percentage of tourists’ most concerned cultural tourism themes is 22.15%, and most of the nearest neighbor indices of the spatial structure of the attractions at different levels are less than 1, which is cohesive. The Moran index of tourists’ footprints in 2022 is 0.106, the footprint coverage rate is 0.26 percentage points higher than that in 2012, and tourists’ positive emotional evaluation of cultural tourism destinations reaches 80%. Based on cultural ecology, realizing the synergistic development of cultural tourism and ecology is the way to innovate cultural tourism.

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Automatic Text Summarization Method Based on Improved TextRank Algorithm and K-Means Clustering

Cited by 5 publications

References 25 publications

Research on the Short-Term Prediction of Offshore Wind Power Based on Unit Classification

Research on the Short-Term Prediction of Offshore Wind Power Based on Unit Classification

An Accuracy Study of Personalized Recommendation System for E-commerce Based on Big Data Analysis

Innovative Cultural Tourism in the Perspective of Cultural Ecology in the Context of Big Data: Theory, Strategy, Practice

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