On finite mixture modeling and model-based clustering of directed weighted multilayer networks

Melnykov, Volodymyr; Sarkar, Shuchismita; Melnykov, Yana

doi:10.1016/j.patcog.2020.107641

Cited by 9 publications

(2 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Model based clustering, is another category of time series clustering methods which was first mentioned and proposed by Wolfe and is based on the idea of defining clusters via a mixture model [40]. Since then, many researchers have contributed to this method of clustering [18,19,39,41,50]. This method has been often regarded as slow especially for large datasets and requires defining parameters that can contribute to the overall inaccuracy.…”

Section: Related Workmentioning

confidence: 99%

Time Series Overlapping Clustering Based on Link Community Detection

Ghahremani,

Amiri

2024

IEEE Access

View full text Add to dashboard Cite

Given the nature of time series and their vast applications, it is essential to find clustering algorithms that depict their real-life properties. Among the features that can hugely effect the options available for time series are overlapping and hierarchical properties. In this paper a novel approach to analyze time series with such features is introduced. Using the two concepts of network construction and link community detection, we have attempted to analyze and identify the mentioned properties of time series using data that is often gathered first hand. The proposed algorithm has been applied using both recent and common similarity measures on ten synthetic time series with hierarchal and overlapping features, alongside various distance measures. When testing the proposed approach, the element-centric measure of similarity indicated a clear increased accuracy for this algorithm, showing the highest accuracy when used alongside the Dynamic Time Warping distance measure. Moreover, the proposed algorithm has been very successful in identifying and forming communities for both large and small time series, thus solving another one of the main issues previous algorithms tended to have.

show abstract

Section: Related Workmentioning

confidence: 99%

Time Series Overlapping Clustering Based on Link Community Detection

Ghahremani,

Amiri

2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Clustering is widely used in many elds, such as image analysis, medical applications, data mining, and bioinformatics [2][3][4][5]. In general, the clustering algorithms can be categorized as partitional clustering [6], densitybased clustering [7], hierarchical clustering [8], model-based clustering [9], grid-based clustering [10], and graph-based clustering [11].…”

Section: Introductionmentioning

confidence: 99%

RFDPC: Density Peaks Clustering Algorithm Based on Resultant Force

Zhang

Huang

et al. 2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Density peaks clustering (DPC) is an efficient and effective algorithm due to its outstanding performance in discovering clusters with varying densities. However, the quality of this method is highly dependent on the cutoff distance. To improve the performance of DPC, the gravitation-based clustering (GDPC) algorithm is proposed. However, it cannot identify the clusters of varying densities. We developed a novel density peaks clustering algorithm based on the magnitude and direction of the resultant force acting on a data point (RFDPC). RFDPC is based on the idea that the resultant forces acting on the data points in the same cluster are more likely to point towards the cluster center. The cluster centers are selected based on the force directional factor and distance in the decision graph. Experimental results indicate superior performance of the proposed algorithm in detecting clusters of different densities, irregular shapes, and numbers of clusters.

show abstract