Stateful DBSCAN Clustering for Consistent Labeling
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Solution Overview
Problem
Existing DBSCAN algorithms are stateless, leading to inconsistent cluster labeling and reduced efficiency in large datasets, particularly in malware detection and analysis where maintaining data order is impractical.
Innovation Solution
A stateful DBSCAN algorithm is implemented by assigning a globally unique identifier (GUID) to each object, allowing the algorithm to persist cluster identities and core point-to-cluster mappings across runs, ensuring deterministic results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional stateless DBSCAN algorithm is used, then the algorithm is simple to implement, but cluster labeling becomes inconsistent across multiple runs on large datasets
Solution Approach 1:
The algorithm segments the clustering process into distinct phases: identifying core points, finding neighbors within epsilon distance, and assigning cluster labels. By segmenting the process and maintaining state information for core points between runs, the algorithm achieves consistent labeling without requiring complete reprocessing of all data.
Solution Approach 2:
The algorithm performs preliminary identification and storage of core points before final cluster assignment. Core points are identified in advance and their positions stored in state, allowing subsequent runs to resume from this pre-computed state rather than recalculating everything, thus ensuring consistency while managing complexity.
2Measurement precision
If full dataset rescanning is performed on every run, then clustering results can be recalculated, but processing time increases significantly for large datasets
Solution Approach 1:
Core points are identified in advance and stored in a persistent state structure. When the algorithm runs again, it retrieves these pre-identified core points rather than recalculating them from scratch, significantly reducing processing time while maintaining clustering accuracy.
Solution Approach 2:
The algorithm maintains continuous state information about core points between runs. This state persistence allows the clustering process to continue from where it left off or to verify existing clusters without complete reprocessing, ensuring both accuracy and efficiency across multiple executions.
3Adaptability or versatility
If data order is changed between runs, then different clustering patterns may emerge, but consistent cluster identities cannot be maintained
Solution Approach 1:
The algorithm uses feedback from previously identified core points to guide subsequent clustering operations. By storing core point positions and using them as reference anchors, the algorithm can accommodate different data orderings while still converging to the same cluster identities, maintaining reliability despite input variability.
Solution Approach 2:
The algorithm changes the parameter being tracked from data order to core point spatial positions. By focusing on the invariant spatial relationships of core points rather than their order in the input data, the algorithm achieves cluster identity consistency regardless of how the data is ordered during processing.
Data Source
AI summary
There is disclosed a computer-implemented method of assigning reputations to objects, including: for respective samples in a dataset, extracting n features from a sample into a feature vector, where n is an integer greater than 1; assigning the sample a globally unique or pseudo-unique identifier (GUID); according to a clustering algorithm, mapping the feature vector into an n-dimensional space and computing distances between the sample and other samples in the n-dimensional space; assigning groups of objects to clusters according to their distances in the n-dimensional space; correlating the clusters to cluster tags based on the GUIDs of the samples, wherein the cluster tags are stored in a persistent cluster tag storage; and assigning the sample a reputation based on reputations of other samples in a cluster that the sample clustered with.