Best Clustering Methods
Choosing the best clustering algorithm for a specific analysis depends on various factors such as the nature of the data, the desired outcomes, and the characteristics of the clusters sought. Here are three common clustering algorithms, each with its strengths and considerations:
K-Means Clustering:
Description: K-Means is a centroid-based algorithm that partitions the dataset into ‘k’ clusters, aiming to minimize the variance within each cluster.
Strengths: Simple and computationally efficient, suitable for large datasets and well-defined clusters.
Considerations: Sensitive to initial cluster centers, assumes clusters are spherical and equally sized.
Hierarchical Clustering:
Description: Hierarchical clustering builds a tree of clusters, either bottom-up (agglomerative) or top-down (divisive), allowing for the creation of a hierarchy of clusters.
Strengths: No need to specify the number of clusters beforehand, provides a dendrogram for visual interpretation.
Considerations: Computationally intensive, less practical for large datasets, and may not perform well with unevenly sized clusters.
DBSCAN (Density-Based Spatial Clustering of Applications with Noise):
Description: DBSCAN groups together data points that are close to each other and separates areas of lower point density.
Strengths: Can identify clusters of arbitrary shapes, robust to outliers, does not require specifying the number of clusters.
Considerations: Sensitivity to parameters like epsilon and min_samples, may struggle with varying density across clusters.
Selection Criteria:
For well-defined, spherical clusters and efficiency, K-Means may be suitable.
If the dataset has a hierarchical structure or the number of clusters is unknown, Hierarchical Clustering is valuable.
DBSCAN is effective when dealing with irregularly shaped clusters and varying cluster densities.
The choice ultimately depends on the characteristics of the fatal-police-shooting dataset and the specific goals of the analysis. It may be beneficial to experiment with multiple algorithms and assess their performance based on the desired outcomes and the nature of the clusters present in the data.
ANOVA test
ANOVA, or Analysis of Variance, is a statistical test used to analyze the differences among group means in a sample. It assesses whether the means of different groups are statistically significantly different from each other. In the context of the fatal-police-shooting dataset, ANOVA can be applied to test whether there are significant differences in a numerical variable (e.g., age) among different categorical groups (e.g., gender, race, or manner_of_death).