Graph theory (GT) concepts are potentially applicable in the field of computer science (CS)
for many purposes. The unique applications of GT in the CS field such as clustering of web …

Medical data is one of the most rewarding and yet most complicated data to analyze. How
can healthcare providers use modern data analytics tools and technologies to analyze and …

In this paper, we examine the main advances registered in the last ten years in Pattern
Recognition methodologies based on graph matching and related techniques, analyzing …

Cluster analysis aims at identifying groups of similar objects, and helps to discover
distribution of patterns and interesting correlations in large data sets. Especially, fuzzy …

In this article, a new symmetry based genetic clustering algorithm is proposed which
automatically evolves the number of clusters as well as the proper partitioning from a data …

In this paper a new multiobjective (MO) clustering technique (GenClustMOO) is proposed
which can automatically partition the data into an appropriate number of clusters. Each …

Hierarchical clustering algorithms provide a set of nested partitions called a cluster
hierarchy. Since the hierarchy is usually too complex it is reduced to a single partition by …

Identifying the optimal cluster number and generating reliable clustering results are
necessary but challenging tasks in cluster analysis. The effectiveness of clustering analysis …

With the greatly increased demand for animal products, the global dairy sector has
experienced rapid expansion and intensification. The correspondingly increasing manure …

Clustering algorithms have evolved to handle more and more complex structures. However,
the measures that allow to qualify the quality of such clustering partitions are rare and have …