Categorical Data Clustering with Block Partitioning and Cluster Tracking
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Solution Overview
Problem
Existing clustering methods for large volumes of data, particularly categorical data, are inefficient and resource-intensive, leading to inaccurate threat classification in cyber security systems, and lack scalability and adaptability to evolving data streams.
Innovation Solution
A clustering process that partitions data into subsets, maps data points onto multi-dimensional categorical vectors, iteratively clusters within blocks, and updates clusters over time using hyperspherical shapes, allowing parallel processing and efficient management of large datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional clustering methods are used to process large volumes of categorical data, then clustering accuracy can be maintained, but computational resources and processing time increase substantially
Solution Approach 1:
The patent divides the large dataset into multiple blocks or partitions, processing each block separately through distributed computing nodes. This segmentation allows the system to handle large volumes of categorical data efficiently while maintaining clustering accuracy through coordinated processing across multiple segments.
Solution Approach 2:
The patent transforms categorical data into a different dimensional representation using dimensionality reduction techniques and mapping to continuous vector spaces. This dimensional transformation enables efficient distance-based clustering algorithms to work with categorical data while reducing computational complexity.
2Measurement precision
If manual review processes are used to assess threat levels, then classification accuracy can be maintained, but human resources and processing time are substantially consumed
Solution Approach 1:
The patent implements automated clustering algorithms that self-organize and classify data without human intervention. The system automatically assesses threat levels by analyzing clustered patterns, eliminating the need for manual review while maintaining high classification accuracy through intelligent algorithmic processing.
Solution Approach 2:
The patent replaces manual human review processes with automated computational clustering systems. The mechanical action of human analysts examining and classifying threats is substituted with algorithmic clustering operations that automatically identify patterns and assess threat levels based on data similarity.
3Productivity
If automated AI processing is used to handle large volumes of emails, then processing speed increases, but classification accuracy may deteriorate
Solution Approach 1:
The patent incorporates feedback mechanisms where clustering results are continuously refined based on identified patterns and anomalies. The system learns from processing outcomes and adjusts clustering parameters to improve accuracy over time, ensuring that automated processing maintains high classification precision while operating at high speed.
Solution Approach 2:
The patent implements dynamic clustering algorithms that adapt to changing data characteristics and threat patterns in real-time. The system dynamically adjusts clustering parameters, thresholds, and groupings based on the evolving nature of the data stream, maintaining high accuracy despite the high-speed automated processing of large volumes of emails.
4Reliability
If clustering is performed on the entire dataset at once, then comprehensive clustering can be achieved, but computational resources and processing time increase substantially
Solution Approach 1:
The patent divides the comprehensive clustering task into smaller sub-tasks processed in parallel across distributed computing nodes. Each node processes a specific block of data using less computational resources, while the overall system achieves complete clustering through coordinated aggregation of results from all segments.
Solution Approach 2:
The patent performs preliminary data preprocessing, filtering, and blocking operations before the main clustering process. Data is pre-organized into manageable blocks with relevant features extracted in advance, reducing the computational burden during the actual clustering operation while ensuring comprehensive coverage of the entire dataset.
Data Source
AI summary
An approach for clustering large sets of categorical data involves iteratively ordering the data points, partitioning the data into blocks based on the ordering, and clustering the data points within each block, where different iterations use different orderings and, thus, different partitionings. In some embodiments, the data points are represented by multi-dimensional categorical vectors, and the orderings are based on permutations of the categorical dimensions. The iterative clustering may be repeated for multiple successive time windows to track the clusters. Various applications of the disclosed clustering approach, including for cyber security, are also described.