Which of the following statements best describes how the Cluster-Based Local Outlier Factor (CBLOF) method detects anomalies?

Question

BlackTom AI · Accepted Answer

To analyze how CBLOF detects anomalies, we must compare each statement to the core idea of the method.
Option 1: 'It detects anomalies by evaluating whether points lie in regions of significantly lower density than their neighbors.' While CBLOF relates to clusters and distances, this description aligns more closely with density-based methods like LOF rather than CBLOF’s cluster-centric approach. It emphasizes local density regions rather than cluster structure and size.
Option 2: 'It assigns outlier scores based on the size of the cluster a point belongs to and its distance to the nearest large cluster if it's in a small one.' This matches the CBLOF framework: points in smaller clusters receive higher anomaly scores based on the cluster size, and the distance to larger clusters further modulates the score, capturing how atypical a point’s placement is relative to the global cluster structure.
Option 3: 'It labels points as anomalies if their distance to their k-nearest neighbors is above a threshold.' This describes a typical k-NN-based anomaly detector, which relies on local neighbor distances, not on cluster sizes and distances to large clusters as CBLOF does.
Option 4: 'It flags points as anomalies if they fall outside the convex hull formed by the majority of data points.' This reflects a convex-hull or boundary-based approach, which CBLOF does not specifically use; CBLOF focuses on cluster-driven scoring rather than convex-hull containment.
In summary, option 2 accurately captures CBLOF’s mechanism, while the others describe distinct anomaly detection paradigms that CBLOF does not primarily rely on.

Which of the following statements best describes how the Cluster-Based Local Outlier Factor (CBLOF) method detects anomalies?Single choice

Similar Questions

The system is designed to ______ deviations from expected performance metrics.

Process anomalies and unexpected failures of manufacturing systems are problems that cause a decrease in the quality of processes and products. What data analytics algorithm can be used to detect anomalies?

How can we handle anomaly detection with respect to variable density clusters in an unsupervised algorithm like k-means?

Which one of the following strategies in anomaly detection does not calculate the distance between data points to determine an anomalous vs. a normal instance?

In IPS, a fundamental technique to determine if an intrusion is in progress in a stable network is:

Consider the system of ODEs \[\dfrac{dx}{dt} = x + y, \qquad \dfrac{dy}{dt} = y\] The general solution of this system of equations is

More Practical Tools for Students Powered by AI Study Helper

Homework AI Solver

Stylized AI Paper Writer

Plagiarism Checker Assistant

Citation AI Academic Writing Tool

In-Class Translation Assistant

AI Note Generator

AI Quiz Answers

Past Exam Questions from University Test Bank

Smart Practice Assistant

Adaptive Practice

Making Your Study Simpler