Using lapply for k-mean clustering of many groups

Introduction

In this article, we will explore how to use the lapply function in R for k-means clustering on multiple datasets. Specifically, we will look at an example where we have 100,000 individuals with trip times and want to cluster each individual into a group based on their trip times.

We will also discuss why the code may be slow and how to improve its performance using parallel processing.

Understanding the Code

The provided R code uses lapply to apply the kmeansfunction (which is not shown in the original post) to each subset of data within the TILPS dataframe. The split function is used to divide the data into subsets based on the values in the "CustomerCard_Num" column.

gr_TILPS <- lapply(split(TILPS, TILPS[,"CustomerCard_Num"]), 
                    FUN=kmeansfunction)

This code will create a list of models, where each model corresponds to one subset of data. The FUN argument specifies the function to be applied to each subset.

However, when we try to cluster all 100,000 individuals, we get an error message indicating that there are more cluster centers than distinct data points.

Error in kmeans(x, 2) : more cluster centers than distinct data points.
5 stop("more cluster centers than distinct data points.") 
4 kmeans(x, 2) 
3 FUN(X[[32861L]], ...) 
2 lapply(split(TILPS, TILPS[, "CustomerCard_Num"]), FUN = kmeansfunction) 
1 clusterhouseholds(TILPStest, 0.25)

This error is due to the fact that the number of cluster centers (k) must be less than or equal to the number of distinct data points.

Possible Solutions

To resolve this issue, we need to ensure that each subset of data has at least as many data points as there are cluster centers. One way to do this is to group the data into subsets based on the size of each subset.

1. Grouping by Subset Size

We can use the group_by function from the dplyr package to group the data into subsets based on their size.

library(dplyr)

gr_TILPS <- lapply(group_by(TILPS, CustomerCard_Num), 
                    FUN=kmeansfunction)

This code will create a new subset of data for each unique value in the "CustomerCard_Num" column. The number of cluster centers (k) can then be set to the square root of the total number of data points.

2. Using Parallel Processing

Another approach is to use parallel processing to speed up the clustering process. We can use the parallel package and the mclust function, which uses multiple CPU cores to perform the clustering.

library(parallel)

gr_TILPS <- lapply(split(TILPS, TILPS[,"CustomerCard_Num"]), 
                    FUN = kmeansfunction, cores = 4)

In this example, we set the number of cores to 4, which can be adjusted based on the available CPU resources.

Checking Which Customer ID is Causing the Error

To check which customer ID is causing the error, we can use the aggregate function to calculate the length of each subset of data and then compare it to the total number of data points.

gr_TILPS_lengths <- aggregate(TILPS$col1, by=list(TILPS$CustomerCard_Num), FUN=length)

which(gr_TILPS_lengths $length > 15)

This code will return a vector of customer IDs that have fewer than 15 data points in each subset.

Improving Performance

The original code may be slow due to the large number of subsets being created. To improve performance, we can use parallel processing and optimize the clustering algorithm.

1. Optimizing Clustering Algorithm

We can use the mclust function from the cluster package, which is optimized for performance and parallel processing.

library(cluster)

gr_TILPS <- lapply(split(TILPS, TILPS[,"CustomerCard_Num"]), 
                    FUN = mclustfunction, cores = 4)

2. Reducing Data Points

We can reduce the number of data points by selecting a subset of data using random sampling or stratified sampling.

set.seed(123)

gr_TILPS <- lapply(sample(TILPS$col1, 10000), 
                    FUN = kmeansfunction)

Conclusion

In this article, we explored how to use lapply for k-means clustering on multiple datasets in R. We discussed possible solutions to the error message and provided code examples to improve performance.

We also touched upon the importance of parallel processing and optimized clustering algorithms to speed up the clustering process.

By following these tips, you can efficiently perform k-means clustering on large datasets using lapply and achieve better performance.

Example Use Cases

Customer Segmentation: Use k-means clustering to segment customers based on their purchase behavior or demographic characteristics.
Image Clustering: Apply k-means clustering to images to group similar pixels together and identify patterns in the data.
Text Analysis: Use k-means clustering to cluster text documents based on their content, topic, or sentiment.

Code

Here is an example code snippet that performs k-means clustering using lapply:

# Load necessary libraries

library(cluster)
library(dplyr)

# Create a sample dataset

set.seed(123)
TILPS <- data.frame(
  CustomerCard_Num = rep(c(1, 2, 3), each = 100),
  col1 = rnorm(300),
  col2 = rnorm(300)
)

# Perform k-means clustering using lapply

gr_TILPS <- lapply(group_by(TILPS, CustomerCard_Num), 
                    FUN = mclustfunction, cores = 4)

# Print the results
print(gr_TILPS)

Note that this code snippet is a simplified example and may require modifications based on your specific use case.

Last modified on 2024-04-15