If we use probabilistic models, we can always evaluate a test set’s likelihood, but this has two drawbacks: firstly, it does not evaluate any clustering found by the model directly, and secondly, it does not a
In the literature, a vast variety of attribute selection methods have been presented. The greedy algorithm starts by selecting an important MSG node (attribute). In-degree, weighted in-degree (where in-degree of a node is the sum of connections related to that node), betweenness centrality, ...
Methods Density of triangles in directed networks We quantify the density of triangles in a directed network with the average local clustering coefficient, \({\bar{c}}_{{{\rm{undir}}}\), computed using the undirected version of the original directed network. From the adjacency matrix of the...
Recently, deep contrastive learning approaches have exhibited substantial capabilities in feature extraction within MVC frameworks. However, the challenge lies in extracting efficient feature representations while ensuring consistency. Moreover, existing deep clustering methods based on contrastive learning often ...
Whereas the existing methods36,40 work only for individual decision matrix and fail to model the multi-experts problem. Also the rate of convergence of the developed clustering model is faster than36,40, can be analyzed from the above comparison. The presented structure also has disadvantages, ...
Graph clustering methods are popular due to their ability to discover clusters with arbitrary shapes. However, with the emergence of large-scale datasets, the efficiency of graph clustering algorithms has become a significant concern. As a result, many researchers have been drawn to the field of ...
Data transformation processes are utilised to adapt attribute values into a format that machines can interpret. Additionally, data normalisation methods are considered to standardise values within a specific range to ensure uniformity in attribute values. 4.3 Data analysis Figure 4 displays the outcomes ...
Finally, compared to existing clustering frameworks, CDSKNN exhibits more stable clustering performance across different feature quantities, effectively balancing computational efficiency and clustering precision. Methods Algorithm design Overview of the CDSKNN workflow CDSKNN leverages three interconnected modules...
In exploratory analytical processes, this value will not be known a priori, but there are methods (such as the elbow method) that can be employed to find the most suitable value. Second, it assumes that the clusters are convex, which means that it cannot deal with irregularly shaped ...
The comparative analysis between univariate and multivariate approaches provides insights into component interactions in ground deformation processes. While FeatTS achieves notable improvements over raw-based methods, its optimal performance requires different explained variance thresholds for MLRD (30% for east...