Getting Started with K-Means Clustering Algorithm

Clustering is a popular exploratory data analysis technique for figuring out how data is structured. The process of discovering subgroups in data so that data points with same cluster are very similar while data points with different clusters are very different. To put it another way, we use a similarity metric like euclidean-based distance or correlation- based distance to try to discover homogeneous subgroups within the data so that data points in each cluster are as comparable as feasible. The application determines whether or not to utilize a similarity metric.

K-means Algorithm

The K-means algorithm divides a dataset into K unique non-overlapping clusters, each with only one data point. It makes an effort to keep data points within clusters as similar as possible while maintaining clusters as different (as far as possible). It places data points in clusters so that sum of squared distances from cluster’s centroid (the arithmetic mean of all data points in that cluster). The data points are similar when there is less variance within clusters.

How K-means algorithm works

K is the number of clusters to specify. Shuffling the dataset and then picking K data points at random for the centroids without replacing them will initialize the centroids. Continue iterating until the centroids do not change, the clustering of data points does not change. Calculate the total of all data points’ squared distances from all centroids. Assign each data point to the cluster that is closest to it (centroid). Calculate the cluster centroids by averaging all of the data points that correspond to each cluster.

Applications

The K-means technique is wide range of applications, including market segmentation, document clustering, image segmentation, and compression. When we do a cluster analysis, we normally want to achieve one of two things:

● Get a good sense of how the data we’re dealing with is structured.

● If we assume there is a wide variance in the behaviors of distinct subgroups, we will cluster-then-predict, where different models will be developed for different subgroups. Clustering patients into distinct subgroups and developing a model for each subgroup to predict the likelihood of having a heart attack is an example of this.

Methods of Evaluation

Clustering analysis lacks a strong evaluation metric to compare the results of various clustering algorithms, unlike supervised learning, where we have the ground truth to evaluate the model’s performance. Furthermore, K-means requires K as an input and does not learn it from data, there is number of clusters we should have in every situation.

Domain knowledge and intuition can sometimes be beneficial, although this is not always the case. Because clusters are utilized in downstream modeling, we can use the cluster-predict methodology to assess how well the models are performing using different K clusters.

Elbow Method

Based on the sum of squared distance (SSE) between data points and their assigned cluster’s centroids, the elbow technique offers us an indication of what a good K number of clusters might be. At the point where SSE begins to flatten out and form an elbow, we pick K. We’ll compute SSE for various K values using the geyser dataset to see where the curve might form an elbow and flatten out.

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