The global optimum in unsupervised machine learning is the best possible solution where the algorithm finds the most accurate patterns or clusters in data. It minimizes (or maximizes) an objective function without getting stuck in a local optimum, which is a "good but not best" solution. Techniques like better initialization, multiple runs, and advanced optimization methods help reach the global optimum.

Canopy Method

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.