K-means clustering is a good algorithm but it's difficult to find the best number of clusters, also it is sensitive to outliers and the data must be scaled if we want to use K-means clustering.

In optimization problems, a global optima (plural: global optima) refers to the best possible solution across the entire solution space. It can be either a global minimum or a global maximum depending on whether the objective is to minimize or maximize a function.

Done

The complete theory of K-Means clustering. I think we should make a maximum of 20 clusters.

Silhoutte Score : ak cluster me majood ak point apny jesy qareebee points sy kitna milta julta ha

Other Method for Optimization of K value is Gap statistic

the "++" in "k++" indicates a smart or improved way to pick the initial points of any cluster. EXAMPLE "Imagine you're trying to group a bunch of points on a graph into, let's say, three groups. The 'k++' thing is a smart way to decide where to start looking for these groups. Instead of just picking starting points randomly, it picks them in a way that makes sure each group gets a good head start and they're not too close to each other. It helps the computer find these groups more efficiently.

What is Global Optima?Uniqueness: In some cases, the objective function might have multiple global optima with the same best value. These are called multimodal optima.Importance: Finding the global optimum is often the ultimate goal of optimization problems, as it represents the best possible solution.Challenges: Finding the global optimum can be challenging, especially in high-dimensional search spaces with complex functions. Many optimization algorithms are only guaranteed to find a local optimum, which might not be the global one.Approaches: Several algorithms and techniques exist for global optimization, each with its own strengths and weaknesses. These include evolutionary algorithms, genetic algorithms, simulated annealing, and particle swarm optimization.

Done