The decline of the number of newly discovered mineral deposits and increase in demand for
different minerals in recent years has led exploration geologists to look for more efficient and …

Clustering, as an unsupervised learning, is aimed at discovering the natural grou**s of a
set of patterns, points, or objects. In clustering algorithms, a significant problem is the …

Clustering is an essential tool in data mining research and applications. It is the subject of
active research in many fields of study, such as computer science, data science, statistics …

Agglomerative hierarchical clustering differs from partition-based clustering since it builds a
binary merge tree starting from leaves that contain data elements to the root that contains the …

Computational prediction of the interaction between drugs and targets is a standing
challenge in the field of drug discovery. A number of rather accurate predictions were …

The monograph Data Clustering: Theory, Algorithms, and Applications was published in
2007. Starting with the common ground and knowledge for data clustering, the monograph …

Data analysis plays an indispensable role for understanding various phenomena. Cluster
analysis, primitive exploration with little or no prior knowledge, consists of research …

Finding useful patterns in large datasets has attracted considerable interest recently, and
one of the most widely studied problems in this area is the identification of clusters, or …

Clustering is the division of data into groups of similar objects. In clustering, some details are
disregarded in exchange for data simplification. Clustering can be viewed as a data …

Clustering, in data mining, is useful for discovering groups and identifying interesting
distributions in the underlying data. Traditional clustering algorithms either favor clusters …