Clustering involves grouping a set of data points into subsets or clusters based on inherent patterns or similarities. It is an Unsupervised Learningtechnique used for tasks like customer segmentation and anomalies detection. The primary goal of clustering is to organize data by grouping similar items.
Applications
- Customer Segmentation: Group customers with similar purchasing behavior or demographics for targeted marketing.
- Image Segmentation: Group pixels in an image based on color or texture to identify objects or regions.
- Anomaly Detection: Identify clusters of normal behavior to detect anomalies that deviate significantly from these clusters.
Methods
Interpretability
Feature Scaling: Essential for bringing features to the same scale, as clusters may appear distorted without it.
from sklearn.preprocessing import scale
from sklearn.preprocessing import MinMaxScalerUse clustering to find correlations between features. Utilize a Dendrograms to visualize the relationship between features.
Clustermap
- Purpose: Identify which features are most similar using dendrograms.
- Visualization: Regions of color show clustering, similar to a heatmap.
- Functionality: Performs clustering on both rows and columns.
Requirements: Input should be numerical; data needs to be scaled.
import seaborn as sns
sns.clustermap(x_scaled, cmap='mako', standard_scale=0) # 0 for rows, 1 for columns