K-means model for Anomaly Detection with Structured Data

K-means is an unsupervised machine learning model commonly used for clustering analysis. It aims to partition a dataset into K distinct clusters, where each data point belongs to the cluster with the nearest mean value. When applied to anomaly detection with structured data, K-means can identify unusual patterns or outliers in the dataset based on their distance from the cluster centroids.

Pros:

• Simple and easy to implement
• Scalable to large datasets
• Effective for identifying cluster-based anomalies

Cons:

• Assumes equal-sized and spherical clusters
• Sensitive to initial centroid initialization
• Requires predefined number of clusters (K)

• Fraud Detection: Identify unusual patterns in financial transactions to detect fraudulent activities.
• Network Intrusion Detection: Identify anomalous network traffic patterns to detect potential security breaches.
• Predictive Maintenance: Identify anomalies in sensor data to detect equipment or system failures before they occur.

• Scikit-learn Documentation: K-means implementation in scikit-learn
• Towards Data Science Blog: Step-by-step guide for implementing K-means with Python
• Medium Article: Anomaly Detection using K-means Clustering in Python

• Dr. Andrew Ng: GitHub
• Dr. Chris McCormick: GitHub
• Dr. Anand Singh: GitHub
• Dr. Sebastian Raschka: GitHub
• Dr. Abhishek Kumar: GitHub