Cluster analysis is a popular technique whose aim is to segregate a set of data points into groups, called clusters where the number of clusters are predefined. Automatic determination of the optimum number of clusters from a dataset is a challenging proposition in the computer vision community. Different metaheuristics are widely used for solving complex optimization problems. In this lecture, the use of the Multilevel Quantum Logic and metaheuristic techniques is explored to design Quantum Metaheuristics, which can be applied to compute optimum number of clusters in a dataset, to obviate human intervention.

The key topics to be discussed in this lecture include Clustering Fundamentals, Quantum Computing Fundamentals, Metaheuristics, Quantum Inspired Metaheuristics, t-test.

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