What are the common partitioning techniques for k-means clustering?

1 Random partitioning

Random partitioning is the simplest and most widely used technique for splitting the data into smaller subsets. It randomly assigns each data point to one of k partitions, where k is the desired number of clusters. The advantage of random partitioning is that it is easy to implement and parallelize, and it does not require any prior knowledge of the data distribution. The disadvantage is that it may create unbalanced or overlapping partitions, which can reduce the accuracy and convergence of k-means clustering.

2 Stratified partitioning

Stratified partitioning is a technique that preserves the proportion of data points from each class or category in each partition. It is useful when the data has labels or attributes that indicate the group membership or the target variable. For example, if the data is about customers and their purchases, stratified partitioning can ensure that each partition has a similar ratio of male and female customers, or of high and low spenders. The advantage of stratified partitioning is that it can improve the representativeness and diversity of each partition, and reduce the bias and variance of k-means clustering. The disadvantage is that it may require more computational resources and time, and it may not work well for data with many classes or categories.

3 Spatial partitioning

Spatial partitioning is a technique that divides the data based on the geometric or spatial properties of the data points. It is useful when the data has a spatial or temporal dimension, such as location, distance, or time. For example, if the data is about events or activities in a city, spatial partitioning can create partitions based on the geographic regions or the time periods of the events. The advantage of spatial partitioning is that it can capture the spatial or temporal patterns and correlations in the data, and enhance the performance and scalability of k-means clustering. The disadvantage is that it may require more domain knowledge and preprocessing, and it may not work well for data with high dimensionality or complexity.

4 Density-based partitioning

Density-based partitioning is a technique that splits the data based on the density or frequency of the data points. It is useful when the data has clusters of different sizes, shapes, or densities, or when the data has outliers or noise. For example, if the data is about images or text, density-based partitioning can create partitions based on the regions or segments that have more or less pixels or words. The advantage of density-based partitioning is that it can adapt to the structure and distribution of the data, and improve the robustness and quality of k-means clustering. The disadvantage is that it may require more parameters and algorithms, and it may not work well for data with low density or uniformity.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?