A two-stage density clustering algorithm

Wang, Min; Zhang, Yingyi; Min, Fan; Deng, Liping; Gao, Lei

doi:10.1007/s00500-020-05028-x

Cited by 8 publications

(3 citation statements)

References 35 publications

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“…On this basis, the user's historical reading records are clustered and classified, and finally the user's Top-n nearest neighbor set is calculated, and books are recommended to it, so as to help students find books suitable for themselves. Density clustering has always been a hot topic in the field of data mining, and the density peak clustering algorithm has pushed the study of density method to a hot trend [26][27][28]. Starting from cluster analysis and recommendation system, this study introduced the theoretical analysis, research status and common methods of related methods in detail, so as to make sufficient preparation for the follow-up work.…”

Section: Discussionmentioning

confidence: 99%

Application of Data Mining Technology with Improved Clustering Algorithm in Library Personalized Book Recommendation System

Lin,

Guan,

Zhang

2023

IJACSA

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The information construction work of university libraries is becoming increasingly perfect. However, the massive amount of data poses significant challenges to the personalized recommendation of books. Cluster analysis has always been an important research topic in data mining technology, and it has a wide range of application fields. Clustering algorithm is a fundamental operation in big data processing, and it also has good application value in personalized recommendation of library books. To improve the personalized service quality of libraries, this study proposes a clustering algorithm based on density noise application spatial clustering. This study introduced a distance optimization strategy and Warhill algorithm to the proposed algorithm, to improve the difficulties in selecting initial parameter neighborhoods and density thresholds in traditional models, as well as computational complexity. Afterwards, this study will integrate the improved algorithm with the density peak algorithm to further improve the operational efficiency of the model. The performance verification of the model demonstrated superior clustering performance. The average accuracy of the proposed model's recommendation is 98.97%, indicating superiority. The practical application results have confirmed that there is a significant similarity between the books read by the readers and the books read by the target readers, and the effectiveness and feasibility of the proposed model have been verified. Therefore, the proposed model can contribute to the personalized recommendation function of libraries and has certain practical significance.

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Section: Discussionmentioning

confidence: 99%

Application of Data Mining Technology with Improved Clustering Algorithm in Library Personalized Book Recommendation System

Lin,

Guan,

Zhang

2023

IJACSA

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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%

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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“…Jiang et al [20] proposed to use of the Gini coefficient to determine the optimal cutoff distance and theuse KNN to find cluster centers, but its accuracy is low on high-dimensional datasets. As the choice of cutoff distance d c has too obvious an impact on the overall clustering result, some other scholars have studied directly omitting the calculation of cutoff distance, such as proposed by Wang et al [21] borrowed the CFDP structure to determine the clustering outcome, generated the primary number, and obtained the final block in two phases without using the cutoff distance parameter. In addition, different ideas were introduced for the DPC to measure density more accurately and reduce the impact of large differences in dataset density on clustering results.…”

Section: Introductionmentioning

confidence: 99%

Density Peaks Clustering Based on Candidate Center and Multi Assignment Policies

Shi

2023

IEEE Access

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Density peak clustering (DPC) the center selection ignores the environment of samples, which can easily result in the incorrect identification of cluster centers. Additionally, a single allocation approach can easily produce the joint effect of allocation mistakes. To address the above issues, a density peaks clustering based on candidate center and multi assignment policies (DPC-CM) is proposed. Firstly, to retain cluster centers with relatively small densities, DPC-CM introduces thresholds to delineate candidate center regions in the decision graph. It also incorporates shared nearest neighbor definitions to ensure proper cluster center selection. Then, the allocation results of the cluster backbone are corrected using shared nearest neighbor information, suppressing allocation cascading errors effectively. Finally, the allocation of cluster backbone points used as the basis for label propagation also improves the allocation efficiency while taking into account the surroundings in which the sample sites are situated. The fault tolerance of the algorithm has also been enhanced by the multi-step allocation strategy. Experimental results on synthetic and UCI datasets validate that the DPC-CM algorithm outperforms comparative algorithms for clustering on complex morphological and unevenly distributed datasets.

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A two-stage density clustering algorithm

Cited by 8 publications

References 35 publications

Application of Data Mining Technology with Improved Clustering Algorithm in Library Personalized Book Recommendation System

Application of Data Mining Technology with Improved Clustering Algorithm in Library Personalized Book Recommendation System

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

Density Peaks Clustering Based on Candidate Center and Multi Assignment Policies

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