Community Detection and Visualization in Complex Network by the Density-Canopy-Kmeans Algorithm and MDS Embedding

Li, Manzhi; Wang, Hongtao; Long, Hang; Ju, Xu; Wang, Bo; Xu, Junlin; Yang, Jialiang

doi:10.1109/access.2019.2936248

Cited by 8 publications

(6 citation statements)

References 38 publications

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“…, mcc n ] (1 ≤ i ≤ n) where n is the length of the array, mcc means the maximal common counts among the whole nodes in the CCB. For each mcc, calculate the whole nodes whose count is greater than or equal to the mcc, then the volume of the CCB can be obtained by Equation (2). Finally, the bicluster with the biggest volume is selected.…”

Section: Initial Ccb Miningmentioning

confidence: 99%

“…Clustering can group samples into different clusters. Traditional one-way clustering methods such as K-means [2] take all features into consideration when calculating the similarity between samples. In many cases samples are similar only under partial features.…”

Section: Introductionmentioning

confidence: 99%

“…Incrementally mining CCB should have the following properties: (1) With the coming of a newly added dataset, the intermediates that are used to generate the previous mined biclusters can be updated to improve the quality of previously mined CCB and new CCB can be mined. (2) The original datasets that have been processed before cannot be scanned/processed again in the incremental mining stage.…”

Section: Introductionmentioning

confidence: 99%

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Incrementally Mining Column Constant Biclusters with FVSFP Tree

2023

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Bicluster mining has been frequently studied in the data mining field. Because column constant biclusters (CCB) can be transformed to be discriminative rules, they have been widely applied in various fields. However, no research on incrementally mining CCB has been reported in the literature. In real situations, due to the limitation of computation resources (such as memory), it is impossible to mine biclusters from very large datasets. Therefore, in this study, we propose an incremental mining CCB method. CCB can be deemed as a special case of frequent pattern (FP). Currently the most frequently used method for incrementally mining frequent patterns is FP tree based method. In this study, we innovatively propose an incremental mining CCB method with modified FP tree data structure. The technical contributions lie in two aspects. The first aspect is that we propose a modified FP tree data structure, namely Feature Value Sorting Frequent Pattern (FVSFP) tree that can be easily maintained. The second aspect is that we innovatively design a method for mining CCB from FVSFP tree. To verify the performance of the proposed method, it is tested on several datasets. Experimental results demonstrated that the proposed method has good performance for incrementally handling a newly added dataset.

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Section: Initial Ccb Miningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Incrementally Mining Column Constant Biclusters with FVSFP Tree

2023

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“…Then, according to the results of rough clustering, k-means algorithm is used to cluster the data, to optimize the clustering results. The specific steps of Canopy algorithm are as follows [24]- [25]:…”

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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“…Algoritme ini telah diimplementasikan oleh Li et. al [12] dalam pengelompokkan komunitas tertentu dan Ananda et al [13] dalam sistem rekomendasi pemilihan peminatan. Langkah-langkah dari algoritme DC sebagai berikut:…”

Section: Algoritme Density Canopy K-meansunclassified

Determination of Initial K-means Centroid in the Process of Clustering Data Evaluation of Teaching Lecturers

Ananda¹,

Yamani²

2020

RESTI

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Decision making about microteaching for lecturers in ITTP with the low teaching quality is only based on three lowest order from teaching values. Consequently, the decision is imprecise, because there is possibility that the lecturers are not three. To get the precise quantity, an analysis is needed to classify the lecturers based on their teaching values. Clustering is one of analyses that can be solution where the popular clustering algorithm is k-means. In the first step, the initial centroids are needed for k-means where they are often randomly determined. To get them, this paper would utilize some preprocessing, namely Silhouette Density Canopy (SDC), Density Canopy (DC), Silhouette (S), Elbow (E), and Bayesian Information Criterion (BIC). Then, the clustering results by using those preprocessing were compared to obtain the optimal clustering. The comparison showed that the optimal clustering had been given by k-means using Elbow where obtain four clusters and 0.6772 Silhouette index value in dataset used. The other results showed that k-means using Elbow was better than k-means without preprocessing where the odds were 0.75. Interpretation of the optimal clustering is that there are three lecturers with the lower teaching values, namely N16, N25, and N84.

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Community Detection and Visualization in Complex Network by the Density-Canopy-Kmeans Algorithm and MDS Embedding

Cited by 8 publications

References 38 publications

Incrementally Mining Column Constant Biclusters with FVSFP Tree

Incrementally Mining Column Constant Biclusters with FVSFP Tree

Adaptive Grid Partitioning Considering Power Supply and Load Distribution

Determination of Initial K-means Centroid in the Process of Clustering Data Evaluation of Teaching Lecturers

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