# Computing initial centroids in k-means

    library(fdacluster)
    true_groups <- c(rep(1, 20), rep(2, 10))

The \(k\)-mean algorithm for both multivariate or functional data requires an initial step in which we select \(k\) observations among our sample to serve as initial centroids for the \(k\) clusters we are looking for.

It is well known and reported that the outcome of the \(k\)-mean algorithm is very sensitive to this initial choice. The functional \(k\)-mean algorithm implementation `fdakmeans()` in the fdacluster package includes a number of seeding strategies that automoatically set the initial centroids which makes the outcome more robust.

## Manual specification of the initial seeds

You can use the optional argument `seeds` which takes in an integer vector in which one can manually specify the indices of the observations that will be used as initial centroids. This vector hence needs to be of size `n_clusters`.

    out_manual <- fdakmeans(
      x = simulated30$x,
      y = simulated30$y,
      n_clusters = 2,
      seeds = c(1, 21),
      warping_class = "affine",
      centroid_type = "mean",
      metric = "normalized_l2",
      cluster_on_phase = FALSE,
      use_verbose = FALSE
    )

    knitr::kable(table(out_manual$memberships, true_groups))

1 | 2
--- | ---
20 | 0
0 | 10

This however leads to an outcome that is very sensitive to the initial choice in `seeds`:

    withr::with_seed(1234, {
      initial_seeds <- replicate(10, sample.int(30, 2, replace = FALSE), simplify = FALSE)
      outs_manual <- lapply(initial_seeds, \(.seeds) {
        fdakmeans(
          x = simulated30$x,
          y = simulated30$y,
          n_clusters = 2,
          seeds = .seeds,
          warping_class = "affine",
          centroid_type = "mean",
          metric = "normalized_l2",
          cluster_on_phase = FALSE,
          use_verbose = FALSE
        )
      })
    })

    tibble::tibble(
      Initialization = initial_seeds |>
        sapply(\(.seeds) paste(.seeds, collapse = ",")),
      `Misclassification Rate (%)` = sapply(outs_manual, \(.clus) {
        tbl <- table(.clus$memberships, true_groups)
        round(min(tbl[1, 1] + tbl[2, 2], tbl[1, 2] + tbl[2, 1]) / 30 * 100, 2)
      })
    ) |>
      knitr::kable()

Initialization | Misclassification Rate (%)
--- | ---
28,16 | 0.00
26,22 | 33.33
5,12 | 36.67
15,9 | 36.67
5,6 | 30.00
16,4 | 26.67
2,7 | 6.67
22,26 | 33.33
6,15 | 30.00
14,20 | 6.67

## \(k\)-means++

The \(k\)-means++ strategy was originally proposed in Arthur and Vassilvitskii (2007). The algorithm is nicely described on the corresponding Wikipedia page as follows:

1. Choose one center uniformly at random among the data points.
2. For each data point \(x\) not chosen yet, compute \(D(x)\), the distance between \(x\) and the nearest center that has already been chosen.
3. Choose one new data point at random as a new center, using a weighted probability distribution where a point \(x\) is chosen with probability proportional to \(D(x)^2\).
4. Repeat Steps \(2\) and \(3\) until \(k\) centers have been chosen.
5. Now that the initial centers have been chosen, proceed using standard \(k\)-means clustering.

Despite the probabilistic nature of the outcome that follows from this strategy, it provides a more robust \(k\)-means procedure:

    withr::with_seed(1234, {
      outs_kpp <- replicate(10, {
        fdakmeans(
          x = simulated30$x,
          y = simulated30$y,
          n_clusters = 2,
          seeding_strategy = "kmeans++",
          warping_class = "affine",
          centroid_type = "mean",
          metric = "normalized_l2",
          cluster_on_phase = FALSE,
          use_verbose = FALSE
        )
      }, simplify = FALSE)
    })

    tibble::tibble(
      Run = 1:10,
      `Misclassification Rate (%)` = sapply(outs_kpp, \(.clus) {
        tbl <- table(.clus$memberships, true_groups)
        round(min(tbl[1, 1] + tbl[2, 2], tbl[1, 2] + tbl[2, 1]) / 30 * 100, 2)
      })
    ) |>
      knitr::kable()

Run | Misclassification Rate (%)
--- | ---
1 | 0.00
2 | 0.00
3 | 0.00
4 | 33.33
5 | 0.00
6 | 0.00
7 | 0.00
8 | 0.00
9 | 0.00
10 | 0.00

## Exhaustive \(k\)-means++

The \(k\)-means++ initialization procedure introduces two additional sources of randomness:

- One for randomly picking the first centroid; and,
- One for picking the remaining centroids based on a weighted probability distribution.

It is easy, with computational cost linear in \(N\), to get rid of the first source of randomness by exhaustively run the \(k\)-means algorithm with \(k\)-means++ initialization strategy using each observation as possible centroid for the first cluster. We call it the exhaustive \(k\)-means++ strategy:

    withr::with_seed(1234, {
      outs_ekpp <- replicate(10, {
        fdakmeans(
          x = simulated30$x,
          y = simulated30$y,
          n_clusters = 2,
          seeding_strategy = "exhaustive-kmeans++",
          warping_class = "affine",
          centroid_type = "mean",
          metric = "normalized_l2",
          cluster_on_phase = FALSE,
          use_verbose = FALSE
        )
      }, simplify = FALSE)
    })

    tibble::tibble(
      Run = 1:10,
      `Misclassification Rate (%)` = sapply(outs_ekpp, \(.clus) {
        tbl <- table(.clus$memberships, true_groups)
        round(min(tbl[1, 1] + tbl[2, 2], tbl[1, 2] + tbl[2, 1]) / 30 * 100, 2)
      })
    ) |>
      knitr::kable()

Run | Misclassification Rate (%)
--- | ---
1 | 0
2 | 0
3 | 0
4 | 0
5 | 0
6 | 0
7 | 0
8 | 0
9 | 0
10 | 0

## Exhaustive search

For completeness, it is also possible to perform an exhaustive search although this should rarely be practical. With our simulated data of \(N = 30\) curves and looking for \(2\), this would be achieved by setting `seeding_strategy = "exhaustive"` but would require to run the algorithm 435 times instead of \(30\) times for the exhaustive \(k\)-means++ strategy which already achieves excellent robustness performances.

## Hierarchial clustering

An alternative is to use hierarchical clustering before \(k\)-means to get good initial candidates for centroids:

    out <- fdakmeans(
      x = simulated30$x,
      y = simulated30$y,
      n_clusters = 2,
      seeding_strategy = "hclust",
      warping_class = "affine",
      centroid_type = "mean",
      metric = "normalized_l2",
      cluster_on_phase = FALSE,
      use_verbose = FALSE
    )

    knitr::kable(table(out_hclust$memberships, true_groups))

1 | 2
--- | ---
20 | 0
0 | 10

This strategy is completely deterministic. In our case, it seems to work well. However, it will perform badly when \(k\)-means clustering and hierarchial clustering are not meant to provide the same clusters in the first place.

Note that we could also implement a DBSCAN initialization strategy in a similar fashion, with the added benefit of autotuning the number of clusters to look for. This is not currently implemented but the user can very well carry that out by hand. First, run `fdadbscan()` on your data, which will tell you how many clusters you should search for. Then use cluster medoids as `seeds` argument for `fdakmeans()`.

## Final recommandations

For functional data sets with a reasonable sample size, we recommend to use the exhaustive \(k\)-means++ strategy. For moderate to large sample sizes, we recommend to switch to the \(k\)-means++ strategy. The latter is the current running default for `fdakmeans()`.

## References

Arthur, D., and S. Vassilvitskii. 2007. “K-Means++ the Advantages of Careful Seeding.” In Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, 1027–35.