Clustering is the unsupervised classification of patterns (observations, data items, or feature
vectors) into groups (clusters). The clustering problem has been addressed in many contexts …

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

K-means is undoubtedly the most widely used partitional clustering algorithm. Unfortunately,
due to its gradient descent nature, this algorithm is highly sensitive to the initial placement of …

Aims To examine and forecast the patterns of diabetes prevalence in synergy with obesity.
Methods Prophet models were employed to forecast the prevalence of diabetes and obesity …

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

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 …

This is the first book to take a truly comprehensive look at clustering. It begins with an
introduction to cluster analysis and goes on to explore: proximity measures; hierarchical …

In this paper, we propose a novel hybrid genetic algorithm (GA) that finds a globally optimal
partition of a given data into a specified number of clusters. GA's used earlier in clustering …

Metaheuristics are the most exciting development in approximate optimization techniques of
the last two decades. They have had widespread successes in attacking a variety of difficult …

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