Cluster analyzes have been widely used in mental health research to decompose inter-
individual heterogeneity by identifying more homogeneous subgroups of individuals …

Graph clustering is a prevalent issue associated with social networks, data mining, and
machine learning; its objective is to detect communities or groups in networks. Inspired by …

In this paper we propose an automatic trajectory data reconciliation to correct common
errors in vision-based vehicle trajectory data. Given “raw” vehicle detection and tracking …

Image-text matching is a fundamental task to bridge vision and language. The critical
challenge lies in accurately learning the semantic similarity between these two …

Classification accuracy for case-based classifiers depends critically on the features used for
case retrieval. Feature extraction from deep learning classifier models has proven a useful …

The concept of rough tolerance set is introduced within growing self-organizing map
(GSOM) to reduce the uncertainty in decision-making by developing a new algorithm …

From the early days of case-based reasoning research, the ability of CBR systems to explain
their decisions in terms of past cases has been seen as an important advantage. However …

Neural network architectures for case adaptation in case-based reasoning (CBR) have
received considerable attention. However, architectural gaps and general questions remain …

The case-based reasoning community is successfully pursuing multiple approaches for
applying deep learning methods to advance case-based reasoning. This “Challenges and …

Similarity-based retrieval of semantic graphs is a core task of Process-Oriented Case-Based
Reasoning (POCBR) with applications in real-world scenarios, eg, in smart manufacturing …