Cluster-Based Anomaly Detection for Nonlinear Data Patterns
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Solution Overview
Problem
Conventional anomaly detection techniques struggle to accurately identify anomalies in complex, high-dimensional datasets with dynamic and non-linear relationships between variables, often resulting in false positives or false negatives due to their inability to adapt to evolving data patterns.
Innovation Solution
The use of a Density-Based Spatial Clustering of Applications with Noise (DB-SCAN) model to partition data points into clusters, followed by determining functional forms using regression, decision tree, or artificial neural network models to identify anomalies based on margin of error and link strength, allowing for dynamic updates as new data patterns emerge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional anomaly detection techniques are used, then the system is simple to implement, but the accuracy of anomaly identification deteriorates due to false positives and negatives in complex, high-dimensional datasets
Solution Approach 1:
The patent segments the data into multiple clusters using DB-SCAN clustering algorithm, where each cluster is processed separately to determine functional forms. This segmentation allows the system to handle complex high-dimensional data by breaking it down into manageable groups, improving anomaly detection accuracy while maintaining computational feasibility through localized analysis rather than global processing
Solution Approach 2:
The patent implements dynamic functional form determination where the system adapts its analysis based on the characteristics of each cluster. The functional forms are determined dynamically using regression, decision trees, or neural networks depending on the cluster's properties, allowing the system to respond to evolving data patterns and maintain high accuracy in dynamic environments
2Reliability
If the system uses multiple models (DB-SCAN, regression, decision tree, neural network) to improve anomaly detection accuracy, then the reliability improves, but the device complexity increases
Solution Approach 1:
The patent employs a multi-functional approach where a single anomaly detection system integrates multiple algorithms (DB-SCAN for clustering, regression for functional form determination, decision trees for classification, and neural networks for pattern recognition). This universal system can handle various data patterns and anomaly types through the same framework, improving reliability by leveraging the strengths of each algorithm while avoiding the need for separate specialized systems
Solution Approach 2:
The system incorporates feedback mechanisms where the determined functional forms are used to identify anomalies, and the anomaly identification results can feed back into refining the functional forms. This iterative feedback process allows the system to continuously improve its reliability by learning from its own performance and adjusting its models accordingly
3Adaptability or versatility
If the system processes high-dimensional data with dynamic patterns, then the adaptability improves, but the processing time increases
Solution Approach 1:
By segmenting high-dimensional data into clusters using DB-SCAN, the system reduces the complexity of processing each subset. This allows the system to maintain adaptability to dynamic patterns within each cluster while reducing overall processing time through parallel or sequential processing of smaller, more manageable data segments rather than processing the entire high-dimensional dataset at once
Solution Approach 2:
The system dynamically adjusts parameters such as cluster density thresholds, functional form selection criteria, and model complexity based on the characteristics of the data being processed. This parameter adaptation allows the system to optimize processing time by using simpler models for straightforward data patterns and more complex models only when necessary, maintaining adaptability while minimizing computational overhead
Data Source
AI summary
Techniques for anomaly detection in data are disclosed. A first plurality of data points that are connected to each other are partitioned into a first plurality of clusters and a corresponding functional form is determined. A second plurality of data points is compared with a threshold number. A data point of the second plurality of data points is identified as an anomaly based on a margin of error if the second plurality of data points is lesser than the threshold number or a functional form of the first plurality of clusters and functional form of the second plurality of clusters if the second plurality of data points is not lesser than the threshold number. A signal corresponding to the data point is generated indicating that the data point is an anomaly.