Although feature selection can simply be used as a solution to high-dimensional problems, elimination process however might lead to some loss of important information that have strong meaning in different context, i.e., in different subspaces. In this light, subspace search [11], a combinatorial...
The authors presented a set of axioms that should be satisfied to give meaning to the generated clusters. 4.2.1.5 Search-based clustering Search-based clustering algorithms are nature-inspired metaheuristic approaches, termed automatic data clustering algorithms. They spontaneously determine the structure ...
In this work we make use of the well-known algorithm DBSCAN (Ester et al.1996) for cluster detection and the recently developed manifold learning algorithm UMAP (McInnes et al.2018) to infer the topological structure of a dataset. To be specific, “inferring the topological structure” as we...
Ideally, a clustering algorithm creates clusters where intra-cluster similarity is very high, meaning the data inside the cluster is very similar to one another. Also, the algorithm should create clusters where the inter-cluster similarity is much less, meaning each cluster contains information that...
For (ii), the values assigned to the arguments estimator_type and estimator_opts have the same meaning as in “Listing 9” section. The keyword "fit_intercept" is set to True (line 6), which amounts to adding the empty cluster in the expansion (refer to “General CE formalism” in ...
monocrit(ndarray):A set of n-1 elements. The statistics used to threshold non-singleton I are called monocrit[i]. The monocrit vector must be monotonic, meaning that given a node c with index I monocrit[i] >= monocrit[j] for all node indices j corresponding to nodes below c. ...
The application of clustering algorithms can be followed by a post-processing step, also known as cluster validation; this step is commonly employed to assess the quality and meaning of the resulting clusters (Theodoridis and Koutroumbas, 2003). Cluster validation plays a key role in assessing ...
natural grouping is present in the data. Clusterability tests are designed to address this problem by capturing underlying cluster structure-or a lack thereof. Widespread use of valid clusterability tests may help orient researchers away from conducting cluster analysis when it lacks practical meaning....
Meaning Machine learning mortality predictions combined with behavioral nudges to clinicians led to an increased rate of serious illness conversations for patients with cancer. Abstract Importance Serious illness conversations (SICs) are structured conversations between clinicians and patients about prognosis, ...
Irish et al.[37]StrengthsThe mobile sink visits the grid with the highest detection event frequency, meaning it provides priority. LimitationsOverhead at cluster head nodes. View article Journal 2022,Ad Hoc Networks NatalieTemene, ...VasosVassiliou ...