# Unsupervised Learning with tidylearn

    library(tidylearn)
    library(dplyr)
    library(ggplot2)

## Introduction

This vignette explores unsupervised learning in tidylearn. All methods wrap established R packages - the algorithms are unchanged, tidylearn simply provides a consistent interface and tidy output.

Wrapped packages include:

- stats (`prcomp()`, `kmeans()`, `hclust()`, `cmdscale()`)
- cluster (`pam()`, `clara()`)
- dbscan for density-based clustering
- MASS (`isoMDS()`, `sammon()`)
- smacof for MDS algorithms

Access raw model objects via `model$fit` for package-specific functionality.

## Dimensionality Reduction

Dimensionality reduction techniques help visualize high-dimensional data and extract key patterns.

### Principal Component Analysis (PCA)

    # Perform PCA on iris data (excluding species)
    model_pca <- tl_model(iris[, 1:4], method = "pca")
    print(model_pca)
    #> tidylearn Model
    #> ===============
    #> Paradigm: unsupervised
    #> Method: pca
    #> Technique: pca
    #>
    #> Training observations: 150

    # Extract variance explained
    variance_explained <- model_pca$fit$variance_explained
    print(variance_explained)
    #> # A tibble: 4 × 5
    #>   component  sdev variance prop_variance cum_variance
    #>   <chr>     <dbl>    <dbl>         <dbl>        <dbl>
    #> 1 PC1       1.71    2.92         0.730          0.730
    #> 2 PC2       0.956   0.914        0.229          0.958
    #> 3 PC3       0.383   0.147        0.0367         0.995
    #> 4 PC4       0.144   0.0207       0.00518        1

    # Cumulative variance explained
    cumsum(variance_explained$prop_variance)
    #> [1] 0.7296245 0.9581321 0.9948213 1.0000000

    # Transform data to principal components
    pca_scores <- predict(model_pca)
    head(pca_scores)
    #> # A tibble: 6 × 5
    #>   .obs_id   PC1    PC2     PC3      PC4
    #>   <chr>   <dbl>  <dbl>   <dbl>    <dbl>
    #> 1 1       -2.26 -0.478  0.127   0.0241
    #> 2 2       -2.07  0.672  0.234   0.103
    #> 3 3       -2.36  0.341 -0.0441  0.0283
    #> 4 4       -2.29  0.595 -0.0910 -0.0657
    #> 5 5       -2.38 -0.645 -0.0157 -0.0358
    #> 6 6       -2.07 -1.48  -0.0269  0.00659

    # Visualize first two components
    pca_plot_data <- pca_scores %>%
      mutate(Species = iris$Species)

    ggplot(pca_plot_data, aes(x = PC1, y = PC2, color = Species)) +
      geom_point(size = 3, alpha = 0.7) +
      labs(
        title = "PCA of Iris Dataset",
        x = paste0(
          "PC1 (", round(variance_explained$prop_variance[1] * 100, 1), "%)"
        ),
        y = paste0(
          "PC2 (", round(variance_explained$prop_variance[2] * 100, 1), "%)"
        )
      ) +
      theme_minimal()

    # Examine loadings (variable contributions)
    loadings <- model_pca$fit$loadings
    print(loadings)
    #> # A tibble: 4 × 5
    #>   variable        PC1     PC2    PC3    PC4
    #>   <chr>         <dbl>   <dbl>  <dbl>  <dbl>
    #> 1 Sepal.Length  0.521 -0.377   0.720  0.261
    #> 2 Sepal.Width  -0.269 -0.923  -0.244 -0.124
    #> 3 Petal.Length  0.580 -0.0245 -0.142 -0.801
    #> 4 Petal.Width   0.565 -0.0669 -0.634  0.524

### Multidimensional Scaling (MDS)

    # Perform MDS
    model_mds <- tl_model(iris[, 1:4], method = "mds", k = 2)
    print(model_mds)
    #> tidylearn Model
    #> ===============
    #> Paradigm: unsupervised
    #> Method: mds
    #> Technique: mds
    #>
    #> Training observations: 150

    # Extract MDS coordinates
    mds_points <- predict(model_mds)
    head(mds_points)
    #> # A tibble: 6 × 2
    #>    Dim1   Dim2
    #>   <dbl>  <dbl>
    #> 1 -2.68  0.319
    #> 2 -2.71 -0.177
    #> 3 -2.89 -0.145
    #> 4 -2.75 -0.318
    #> 5 -2.73  0.327
    #> 6 -2.28  0.741

    # Visualize MDS
    mds_plot_data <- mds_points %>%
      mutate(Species = iris$Species)

    ggplot(mds_plot_data, aes(x = Dim1, y = Dim2, color = Species)) +
      geom_point(size = 3, alpha = 0.7) +
      labs(title = "MDS of Iris Dataset") +
      theme_minimal()

## Clustering

Clustering algorithms group similar observations together without using labels.

### K-means Clustering

    # Perform k-means with k=3
    model_kmeans <- tl_model(iris[, 1:4], method = "kmeans", k = 3)
    print(model_kmeans)
    #> tidylearn Model
    #> ===============
    #> Paradigm: unsupervised
    #> Method: kmeans
    #> Technique: kmeans
    #>
    #> Training observations: 150

    # Extract cluster assignments
    clusters <- model_kmeans$fit$clusters
    head(clusters)
    #> # A tibble: 6 × 2
    #>   .obs_id cluster
    #>   <chr>     <int>
    #> 1 1             3
    #> 2 2             3
    #> 3 3             3
    #> 4 4             3
    #> 5 5             3
    #> 6 6             3

    # Compare clusters with actual species
    table(Cluster = clusters$cluster, Species = iris$Species)
    #>        Species
    #> Cluster setosa versicolor virginica
    #>       1      0          2        36
    #>       2      0         48        14
    #>       3     50          0         0

    # Visualize clusters using PCA
    cluster_viz <- pca_scores %>%
      mutate(
        Cluster = as.factor(clusters$cluster),
        Species = iris$Species
      )

    ggplot(cluster_viz, aes(x = PC1, y = PC2, color = Cluster, shape = Species)) +
      geom_point(size = 3, alpha = 0.7) +
      labs(title = "K-means Clusters vs True Species") +
      theme_minimal()

    # Access cluster centers
    centers <- model_kmeans$fit$centers
    print(centers)
    #> # A tibble: 3 × 5
    #>   cluster Sepal.Length Sepal.Width Petal.Length Petal.Width
    #>     <int>        <dbl>       <dbl>        <dbl>       <dbl>
    #> 1       1         6.85        3.07         5.74       2.07
    #> 2       2         5.90        2.75         4.39       1.43
    #> 3       3         5.01        3.43         1.46       0.246

### PAM (K-medoids)

PAM is more robust to outliers than k-means:

    # Perform PAM clustering
    model_pam <- tl_model(iris[, 1:4], method = "pam", k = 3)
    print(model_pam)

    # Extract clusters
    clusters_pam <- model_pam$fit$clusters
    table(Cluster = clusters_pam$cluster, Species = iris$Species)

### Hierarchical Clustering

    # Perform hierarchical clustering
    model_hclust <- tl_model(iris[, 1:4], method = "hclust")
    print(model_hclust)
    #> tidylearn Model
    #> ===============
    #> Paradigm: unsupervised
    #> Method: hclust
    #> Technique: hclust
    #>
    #> Training observations: 150

    # Plot dendrogram
    plot(model_hclust$fit$model,
         labels = FALSE,
         main = "Hierarchical Clustering of Iris")

    # Cut tree to get clusters
    k <- 3
    clusters_hc <- cutree(model_hclust$fit$model, k = k)
    table(Cluster = clusters_hc, Species = iris$Species)
    #>        Species
    #> Cluster setosa versicolor virginica
    #>       1     50          0         0
    #>       2      0         50        14
    #>       3      0          0        36

    # Visualize hierarchical clusters
    hc_viz <- pca_scores %>%
      mutate(
        Cluster = as.factor(clusters_hc),
        Species = iris$Species
      )

    ggplot(hc_viz, aes(x = PC1, y = PC2, color = Cluster)) +
      geom_point(size = 3, alpha = 0.7) +
      labs(title = "Hierarchical Clustering Results") +
      theme_minimal()

### DBSCAN (Density-Based Clustering)

DBSCAN can find arbitrarily shaped clusters and identify outliers:

    # Perform DBSCAN
    model_dbscan <- tl_model(iris[, 1:4], method = "dbscan", eps = 0.5, minPts = 5)
    print(model_dbscan)

    # Extract clusters (0 = noise/outliers)
    clusters_dbscan <- model_dbscan$fit$clusters
    table(clusters_dbscan$cluster)

    # Compare with species
    table(Cluster = clusters_dbscan$cluster, Species = iris$Species)

### CLARA (for Large Datasets)

CLARA is efficient for large datasets:

    # Create larger dataset
    large_data <- iris[rep(seq_len(nrow(iris)), 10), 1:4]

    # Perform CLARA
    model_clara <- tl_model(large_data, method = "clara", k = 3, samples = 5)
    print(model_clara)

    # Extract clusters
    clusters_clara <- model_clara$fit$clusters

## Choosing the Number of Clusters

### Elbow Method

    # Try different values of k
    k_values <- 2:8
    within_ss <- numeric(length(k_values))

    for (i in seq_along(k_values)) {
      k <- k_values[i]
      model <- tl_model(iris[, 1:4], method = "kmeans", k = k)
      within_ss[i] <- model$fit$model$tot.withinss
    }

    # Plot elbow curve
    elbow_data <- data.frame(k = k_values, within_ss = within_ss)

    ggplot(elbow_data, aes(x = k, y = within_ss)) +
      geom_line(linewidth = 1) +
      geom_point(size = 3) +
      labs(
        title = "Elbow Method for Optimal k",
        x = "Number of Clusters (k)",
        y = "Total Within-Cluster Sum of Squares"
      ) +
      theme_minimal()

## Predicting on New Data

### Clustering New Observations

    # Train clustering model
    model_train <- tl_model(iris[1:100, 1:4], method = "kmeans", k = 3)

    # Predict cluster assignments for new data
    new_data <- iris[101:150, 1:4]
    new_clusters <- predict(model_train, new_data = new_data)

    head(new_clusters)
    #> # A tibble: 6 × 1
    #>   cluster
    #>     <int>
    #> 1       3
    #> 2       3
    #> 3       3
    #> 4       3
    #> 5       3
    #> 6       3

### Transforming New Data with PCA

    # Train PCA model
    pca_train <- tl_model(iris[1:100, 1:4], method = "pca")

    # Transform new data
    new_pca <- predict(pca_train, new_data = new_data)
    head(new_pca)
    #> # A tibble: 6 × 5
    #>   .obs_id   PC1    PC2     PC3    PC4
    #>   <chr>   <dbl>  <dbl>   <dbl>  <dbl>
    #> 1 1        3.38 -1.28  -1.59    0.332
    #> 2 2        2.53  0.265 -0.849   0.167
    #> 3 3        3.78 -1.41  -0.0897  0.150
    #> 4 4        2.85 -0.514 -0.495  -0.151
    #> 5 5        3.39 -0.899 -0.797   0.201
    #> 6 6        4.42 -1.85   0.295  -0.128

## Combining Multiple Techniques

### PCA followed by Clustering

    # Reduce dimensions with PCA
    pca_model <- tl_model(iris[, 1:4], method = "pca")
    pca_data <- predict(pca_model)

    # Select first 2 components
    pca_reduced <- pca_data %>% select(PC1, PC2)

    # Cluster in reduced space
    kmeans_pca <- tl_model(pca_reduced, method = "kmeans", k = 3)
    clusters_pca <- kmeans_pca$fit$clusters

    # Visualize
    viz_combined <- pca_data %>%
      mutate(
        Cluster = as.factor(clusters_pca$cluster),
        Species = iris$Species
      )

    ggplot(viz_combined, aes(x = PC1, y = PC2, color = Cluster, shape = Species)) +
      geom_point(size = 3, alpha = 0.7) +
      labs(title = "Clustering in PCA Space") +
      theme_minimal()

## Practical Applications

### Customer Segmentation

    # Simulate customer data
    set.seed(42)
    customers <- data.frame(
      age = rnorm(200, 40, 15),
      income = rnorm(200, 50000, 20000),
      spending_score = rnorm(200, 50, 25)
    )

    # Standardize features
    customers_scaled <- scale(customers) %>% as.data.frame()

    # Cluster customers
    customer_segments <- tl_model(customers_scaled, method = "kmeans", k = 4)
    customers$segment <- customer_segments$fit$clusters$cluster

    # Visualize segments
    ggplot(customers,
           aes(x = income, y = spending_score,
               color = as.factor(segment))) +
      geom_point(size = 3, alpha = 0.7) +
      labs(
        title = "Customer Segmentation",
        color = "Segment"
      ) +
      theme_minimal()

### Feature Extraction

    # Use PCA for feature extraction
    pca_features <- tl_model(mtcars, method = "pca")

    # Keep components explaining 90% of variance
    var_exp <- pca_features$fit$variance_explained
    cumulative_var <- cumsum(var_exp$prop_variance)
    n_components <- which(cumulative_var >= 0.90)[1]

    cat("Components needed for 90% variance:", n_components, "\n")
    #> Components needed for 90% variance: 4
    cat("Original features:", ncol(mtcars), "\n")
    #> Original features: 11
    dim_reduction <- round(
      (1 - n_components / ncol(mtcars)) * 100, 1
    )
    cat("Dimension reduction:", dim_reduction, "%\n")
    #> Dimension reduction: 63.6 %

## Best Practices

1. Scale your data before clustering or PCA for fair feature comparison
2. Determine optimal k using elbow method or silhouette analysis
3. Try multiple methods - different algorithms work better for different data
4. Visualize results to understand cluster structure
5. Consider domain knowledge when interpreting clusters
6. Use PCA for visualization when data has more than 2-3 dimensions

## Summary

tidylearn provides comprehensive unsupervised learning tools:

- Dimensionality Reduction: PCA, MDS for visualization and feature extraction
- Clustering: K-means, PAM, CLARA, hierarchical, DBSCAN
- Unified API: Same `tl_model()` function for all methods
- Prediction Support: Transform new data with learned patterns
- Flexible: Works with different data types and sizes

    # Complete unsupervised workflow
    workflow_data <- iris[, 1:4]

    # 1. Reduce dimensions
    pca_final <- tl_model(workflow_data, method = "pca")

    # 2. Cluster in reduced space
    pca_coords <- predict(pca_final) %>% select(PC1, PC2)
    clusters_final <- tl_model(pca_coords, method = "kmeans", k = 3)

    # 3. Visualize
    final_viz <- pca_coords %>%
      mutate(
        Cluster = as.factor(clusters_final$fit$clusters$cluster),
        Species = iris$Species
      )

    ggplot(final_viz, aes(x = PC1, y = PC2, color = Cluster)) +
      geom_point(size = 3, alpha = 0.7) +
      labs(title = "Complete Unsupervised Workflow") +
      theme_minimal()