Co-Clustering Ensemble Based on Bilateral K-Means Algorithm

Yang, Hui; Han, Peng; Zhu, Jianyong; Nie, Feiping

doi:10.1109/access.2020.2979915

Cited by 8 publications

(5 citation statements)

References 40 publications

(38 reference statements)

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“…Fuzzy parameter identification of portable multidimensional control software is carried out by using the fuzzy semantic feature reconstruction method, and the feature quantity of associated information of data in portable multidimensional control software is extracted. e difference degree function of fuzzy semantic feature reconstruction is expressed as follows (3) [14]:…”

Section: Portable Multidimensional Control Software Testmentioning

confidence: 99%

Application of Software Data Analysis Model Based on K-Means Clustering Algorithm

Qiu

2022

Security and Communication Networks

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In order to improve the anomaly detection ability of portable multidimensional control software test data, a software test data anomaly detection method based on K-means clustering is proposed. The abnormal data distribution structure model of portable multidimensional control software testing is constructed. The fuzzy semantic feature reconstruction method is adopted to identify the fuzzy parameters of portable multidimensional control software and extract the feature quantity of associated information. According to the evolution distribution of associated features, the joint combination feature analysis method is adopted to realize the fuzzy clustering center detection of abnormal data of portable multidimensional control software test, and the fusion of abnormal feature distribution is carried out to complete the joint multidimensional feature detection. The K-means clustering method is used for effective data combination control in portable multidimensional control software to extract and detect abnormal features of test data in portable multidimensional control software. Experimental results show that the accuracy of anomaly detection of software test data by the proposed method is always greater than 0.8. Conclusion. Using this method to detect abnormal data of portable multidimensional control software test has higher accuracy and better detection performance.

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Section: Portable Multidimensional Control Software Testmentioning

confidence: 99%

Application of Software Data Analysis Model Based on K-Means Clustering Algorithm

Qiu

2022

Security and Communication Networks

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“…However, the general K-means clustering algorithm needs to determine the number of clustering centers first, and the specific number is unknown in most cases. However, if the number of clustering centers is not set properly, the final clustering result will have a large error [21]- [23]. Therefore, Canopy algorithm was used to improve the K-means clustering algorithm, and the improved K-means clustering algorithm was used to perform cluster analysis on the existing renewable energy and load data.…”

Section: A Improve K-means Clustering Algorithmmentioning

confidence: 99%

Adaptive Grid Partitioning Considering Power Supply and Load Distribution

Yao

Zhang

et al. 2022

IEEE Access

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Renewable energy such as wind power, hydro-power and photovoltaic are connected to the power system as distributed power sources will increase the instability of power system operation. To ensure the stable operation of the power system after renewable energy and load access, it is necessary to properly partition the power grid. A grid partition method considering renewable energy access and load fluctuation is proposed. First, cluster analysis was carried out on the operation scenarios of renewable energy and load by using the improved K-means algorithm, and several operation scenarios of power system were obtained. The three operation scenarios with the highest probability were selected as the three normal operation states of power system. Then, the power system is partitioned, and the comprehensive flexibility evaluation indexes are proposed from the perspectives of regional supply and demand balance and inter-regional transmission capacity. The flexibility evaluation indexes from these two perspectives are calculated under three operating states, and the two flexibility evaluation indexes are weighted to evaluate the comprehensive flexibility of the power system after the partition. The comprehensive flexibility index of the power system is different under different partition strategies. The partition strategy with the best evaluation of the comprehensive flexibility index is chosen as the final partition strategy. Finally, according to the historical data of renewable energy and load in a certain region, the IEEE 39-bus system with renewable energy access and load fluctuation is partitioned under different partitioning strategies, and the comprehensive flexibility of the partitioned power system is evaluated, and the final partitioning strategy is determined according to the comprehensive flexibility index of the power system.INDEX TERMS Adaptive partition, renewable energy, power system, flexibility evaluation index.

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“…Here, the nodes match the data of the samples and the weights of the edges are similar. The final results of clustering are obtained by using the METIS algorithm by using a connected graph 22 . An additional method for the pairwise matrix of samples is homogeneous to consider the matching between the samples of two‐cluster, which is used as a binary matrix subscribed 23,24 .…”

Section: Literature Reviewmentioning

confidence: 99%

“…The final results of clustering are obtained by using the METIS algorithm by using a connected graph. 22 An additional method for the pairwise matrix of samples is homogeneous to consider the matching between the samples of two-cluster, which is used as a binary matrix subscribed. 23,24 Abawajy et al 25 presented this membership as a bipartite, and they named the hybrid bipartite graph formulation (HBGF) algorithm, which stands for the algorithm to formulate a hybrid graph.…”

Section: 𝛿 =mentioning

confidence: 99%

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An ensemble clustering method based on consistency cluster consensus approach and MapReduce model

Liu

Osmani

2022

Trans Emerging Tel Tech

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Ensemble clustering is an efficient unsupervised learning technique that has attracted a lot of attention. The purpose of this technique is to aggregate the results of several basic clustering algorithms in order to create a better clustering. This is not only possible, but has been developed with many techniques in recent years. However, there are still challenges such as similarity measure, agreement function and how to implement clustering algorithms. To address these challenges, this article proposes an ensemble clustering algorithm based on the consistency cluster consensus approach and the MapReduce model. In addition, the proposed algorithm is equipped with a new membership similarity measure. The consistency cluster consensus is defined as a new agreement function for the consensus of the results of the basic clustering methods. Besides, the proposed similarity measure consists of two factors: one is cluster similarity and the another is membership similarity. The process of the proposed ensemble clustering method is summarized in four steps. In the first step, partitions are generated by applying a number of individual clustering algorithms to the data. The second step is to convert the primary clusters from the partitions to a binary representation. The third step is to identify the candidate primary clusters for the consensus, where a subset with highest similarity between the clusters is emphasized. In the fourth step, the consistency cluster consensus is performed through a new agreement function. In addition, we use the MapReduce model to implement basic clustering algorithms to reduce computational complexity. Our method is evaluated on several real-world datasets and their performance was compared with other clustering ensemble methods, such as TWCE and RDPC-DSS. Experiments show that our method is more efficient and accurate and is 7% to 15% better than the best method compared. INTRODUCTIONData mining is the process of discovering regular and hidden patterns and processes in big data and distributed using a wide range of algorithms based on mathematical and statistical sciences. 1,2 These algorithms are usually applied to numeric and nontextual values and textual algorithms are used for textual data. Data mining uses sciences such as artificial intelligence, machine learning, statistics, operational research and database management to build models

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Co-Clustering Ensemble Based on Bilateral K-Means Algorithm

Cited by 8 publications

References 40 publications

Application of Software Data Analysis Model Based on K-Means Clustering Algorithm

Application of Software Data Analysis Model Based on K-Means Clustering Algorithm

Adaptive Grid Partitioning Considering Power Supply and Load Distribution

An ensemble clustering method based on consistency cluster consensus approach and MapReduce model

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