Contents [hide]
Advantages and Disadvantages of Hierarchical Clustering
An unsupervised machine learning method used in data science to cluster comparable data points is hierarchical clustering. Hierarchical clustering does not need selecting the number of clusters in advance, unlike k-means. Instead, it produces a dendrogram of clusters to show the data’s structure. Hierarchical clustering has restrictions but benefits. This data science article discusses hierarchical clustering pros and cons.
Advantages of Hierarchical Clustering
1. No need to specify cluster number
Hierarchical clustering’s ability to skip predefining cluster sizes is a major benefit. This helps when the data structure is uncertain. The program creates a cluster hierarchy, letting the user choose the best number by evaluating the dendrogram. This flexibility makes hierarchical clustering ideal for exploratory data analysis.
2.Simple to Understand
The links between data points and clusters are shown in a dendrogram created using hierarchical clustering. This image simplifies data interpretation and natural groups. The data’s structure can be seen by cutting the dendrogram at different levels to get different cluster counts.
3.Handles Any Cluster Shape
Hierarchical clustering can find clusters of any shape, unlike k-means, which requires spherical and similar-sized clusters. This makes it suited for complex datasets with uneven cluster forms or poor separation.
4.Good for Small Datasets
Hierarchical clustering works well for small to larger datasets. For datasets with few observations, the technique computes pairwise distances between all data points to produce detailed and reliable results. This makes it ideal for manageable dataset applications.
5.Insensitivity to Initial Conditions
Hierarchical clustering is deterministic, unlike k-means, which depends on centroids. The approach does not use random initialization, therefore results are consistent between runs. When replication matters, this reliability helps.
6.Captures Hierarchies
Hierarchical clustering uniquely captures data hierarchies. Hierarchical clustering can organize species into genera, families, and orders in biological taxonomy, reflecting life’s natural hierarchy. This makes it handy in hierarchical data domains.
7.Distance Metric Flexibility
Hierarchical clustering supports Euclidean, Manhattan, cosine similarity, and single, complete, average, and Ward’s method linkage criteria. This flexibility lets users customize the algorithm to their data, boosting clustering results.
Disadvantages of Hierarchical Clustering
1. Computationally Expensive: The computational complexity of hierarchical clustering is a major downside. The algorithm must calculate pairwise distances between all data points, which takes O(n²) time, where n is the number of data points. This can be too sluggish and resource-intensive for huge datasets. Big data applications typically make hierarchical clustering impractical.
2.Outlier and Noise Sensitivity: Data noise and outliers affect hierarchical clustering. Since the approach uses pairwise distances, even a few outliers might significantly alter grouping results. This can cause inaccurate clusters or dendrogram distortion.
3.Trouble with Large Datasets: Hierarchical clustering is too computationally intensive for huge datasets. The approach is difficult to apply to datasets with millions of observations since memory needs increase quadratically with data points. In such circumstances, k-means or DBSCAN clustering is better.
4.Reversible: Hierarchical clustering cannot undo cluster mergers or splits. A mistake made early in the clustering process can propagate through the hierarchy, resulting in inferior results. However, partitioning algorithms like k-means allow cluster refining iteratively.
5.Subjectivity in Cluster Number Selection: Hierarchical clustering does not need the user to select the number of clusters, however dendrogram analysis can be subjective. Dendrogram interpretation might vary, resulting in conflicting findings. Subjectivity can hinder objective decision-making.
6.Distance and linkage criteria dependence: Distance metric and connection requirement greatly affect hierarchical clustering outcomes. An incorrect distance measure or connection approach can degrade clustering. Single connection can cause “chaining,” where clusters are merged based on a single pair of close points, while complete linkage can yield compact but overly distanced clusters.
7.Unsuitable for HD data: The “curse of dimensionality.” makes hierarchical clustering perform badly on high-dimensional data. In high-dimensional domains, point distances are less significant, making cluster identification challenging for the algorithm. The data may need to be preprocessed using dimensionality reduction methods like PCA, adding complexity.
8.Inability to Scale: Hierarchical clustering cannot handle huge datasets or real-time applications. The algorithm’s computational and memory constraints make it unsuitable for continuous data generation or fast results. Scalable clustering approaches are preferable here.
Hierarchical Clustering Uses
Hierarchical clustering is employed in many fields, despite its drawbacks:
Biology and bioinformatics: Clustering gene expression data, phylogenetic trees, protein sequence analysis.
Social Network Analysis: Finding communities in social networks.
Processing images: Segmenting them into comparable sections.
Market segmentation: By demography or purchasing behavior.
Document Clustering: For thematically grouping huge documents.
Conclusion
Hierarchical clustering can manage various cluster shapes, give interpretable findings, and capture hierarchical linkages. Its high computing cost, noise sensitivity, and difficulty managing huge datasets are drawbacks. Data scientists must consider the data, problem, and trade-offs before using hierarchical clustering. Hierarchical clustering works well for small to medium-sized hierarchical datasets. For huge or high-dimensional data, various clustering approaches may be better. Data scientists can make better decisions and use hierarchical clustering in their analysis by understanding Advantages and Disadvantages of Hierarchical Clustering.