Distributed KNN Framework for Scalable Clustering
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Solution Overview
Problem
Current clustering techniques, such as HDBSCAN, face scalability issues due to computationally expensive local join operations for identifying k-nearest neighbors, particularly in high-dimensional data sets, limiting their effectiveness beyond a million data points.
Innovation Solution
A distributed framework for computing k-nearest neighbors using local join operations across multiple executors and a server, enabling parallel processing and reducing time and bandwidth requirements through index-based communication and iterative updates of KNN lists.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional HDBSCAN clustering is used to ensure accurate k-nearest neighbor identification, then clustering accuracy is improved, but computational cost and processing time increase significantly
Solution Approach 1:
The patent divides the computational task into multiple independent partitions, each processed by a separate worker. Each worker handles a subset of data points and performs local join operations independently, then results are merged. This segmentation reduces the time complexity from O(n²) to O(n/p) where p is the number of partitions, directly addressing the time-cost contradiction while maintaining accuracy through proper result integration.
Solution Approach 2:
The patent pre-computes and stores k-nearest neighbor lists for all data points before the actual clustering operation. This preliminary action allows the clustering algorithm to reuse these pre-computed results without performing expensive distance calculations during the clustering phase, significantly reducing processing time while preserving clustering accuracy.
2Measurement precision
If traditional HDBSCAN clustering is used to ensure accurate k-nearest neighbor identification, then clustering accuracy is improved, but computational resources and complexity increase
Solution Approach 1:
The patent segments the computational workload across multiple independent workers, each handling a partition of the data. This divides the complex O(n²) local join operation into p independent O(n/p) operations, reducing the computational complexity burden on any single processor while maintaining overall accuracy through coordinated result merging.
Solution Approach 2:
The patent replaces the traditional sequential mechanical computation approach with a parallel distributed computing model. By substituting the single-threaded computational mechanism with a multi-worker parallel system, the overall computational complexity is reduced while maintaining the same clustering accuracy through coordinated communication and result integration.
3Measurement precision
If data set size is increased to improve clustering comprehensiveness, then clustering quality is improved, but scalability deteriorates due to expensive local join operations
Solution Approach 1:
The patent implements segmentation by dividing large data sets into manageable partitions that can be processed in parallel. This allows the system to scale to larger data sets by simply adding more workers, as each worker independently processes its partition with reduced computational burden, thereby improving both clustering quality and scalability simultaneously.
Solution Approach 2:
The patent introduces a new dimension of parallelism by distributing computations across multiple workers operating simultaneously. This dimensional transformation from sequential to parallel processing enables the system to handle larger data sets efficiently, improving scalability without sacrificing clustering quality.
4Measurement precision
If more computational resources are allocated to perform complete local join operations, then k-nearest neighbor accuracy is improved, but processing time and resource consumption increase
Solution Approach 1:
The patent segments the computational task across multiple workers, each performing local join operations on a subset of data. This segmentation allows the system to achieve the same k-nearest neighbor accuracy with distributed resources, reducing the energy consumption and resource burden on any single processor while maintaining overall computational precision.
Data Source
AI summary
The present teaching relates to method, system, medium, and implementations for identifying k nearest neighbors. One or more KNN lists corresponding to one or more source data points are received. Each KNN list includes K neighbors of a source data point and each of the K neighbors is a data point represented by an index. Neighbor pairs and reverse neighbor pairs are generated based on the one or more KNN lists. The neighbor pairs and reverse neighbor pairs having the same source data point are grouped to generate a grouped pairs of neighbors for the source data point. A local join operation is performed based on grouped pairs of neighbors for each source data point to generate a combined neighborhood for the source data point, which is then sent to a KNN server, where combined neighborhoods generated by multiple local join executors are integrated to update a plurality of global KNN lists.