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?

Question

BlackTom AI · Accepted Answer

To tackle the question, I’ll compare how each strategy typically handles anomaly detection and whether distance between data points is a central mechanism.
Option 1: Proximity-based approaches. These methods (like nearest neighbors) explicitly rely on measuring distances between data points to decide if a point is anomalous, because points far from their neighbors are flagged as unusual. Thus, this option uses distance between data points.
Option 2: Density-based approaches. These techniques assess how dense the surrounding region is; anomalies are in low-density areas, which again involves comparing a point to its local neighborhood and effectively depends on distance or neighborhood proximity. Therefore, distance concepts are core here.
Option 3: Clustering-based approaches. Clustering groups similar points together, and points that don’t fit well into clusters (e.g., are far from cluster centroids or form tiny clusters) are considered anomalies. This relies on distance to clusters or centroids as well, so distance between points and centers is part of the method.
Option 4: Statistical approaches. Statistical methods model the data distribution (e.g., Gaussian, Poisson, or nonparametric densities) and flag points that have low probability under the model or that lie beyond specified thresholds. These do not primarily compute distances between data points to determine anomalies; instead, they focus on whether a value is likely under the assumed distribution or falls outside expected ranges.
In summary, the first three options operationalize anomaly detection through distance or neighborhood relationships, while statistical approaches base decisions on probability under a distribution rather than pairwise distances.
Therefore, the strategy that does not calculate the distance between data points to determine anomalous versus normal instances is statistical approaches.

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?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?

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

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

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