Besides that, we employ a dispersing mechanism in the second CIEFA model to dispatch fireflies with high similarities to new positions out of the close neighbourhood to perform global exploration. One the many benefits of being a small School are that we know each and every girl individually and as such, there is a constant flow of communication between staff, parents and pupils. In addition, its diversification as a global metaheuristic can lead to reduced speed, as well as an associated decrease in the rate of convergence when applied to solve problems with large number of variables such as data clustering problems.

A cluster can be defined as a collection of objects which are “homogeneous” between them and are “heterogeneous” to the objects belonging to other clusters. Added to it our top notch and latest modern equipments and exceptionally talented and experienced professionals, our center is the dream destination for all those who wish to learn the sport and become a champion in it.Cluster analysis can be defined as applying clustering algorithms with the goal of finding hidden patterns or groupings in a dataset. The ‘Involved’ Steering Group meets termly to plan and organise events for parents including social occasions, including wine tasting, barbeques and the spectacular annual Summer Ball.

The group welcomes new parents to the Badminton community and works with the School to enrich the girls’ experience here. The minimum Redundancy Maximum Relevance (mRMR)-based feature selection method is also adopted to reduce feature dimensionality.To mitigate the aforementioned drawbacks, an improved firefly algorithm is hybridized with the well-known particle swarm optimization algorithm to solve automatic data clustering problems.

The sensor nodes consume more energy for the process of data communication and also affect the network lifetime. They take part in Summer and Christmas community events, the biennial Careers Fair, the biennial Opportunities Fair and the Badminton Boardroom scheme. The chapter also discusses why FA is to be more useful for clustering over other methods and what features made it more suitable for handling the clustering problem compared with other meta-heuristics.So, developing methods and algorithms for analyzing team sports has become one of the most popular topics among data scientists.