Distributed Data Clustering via Independent Node Segmentation
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Solution Overview
Problem
Current data classification systems face scalability issues due to the single node approach, which leads to high costs and performance problems when handling large datasets, as they require more powerful machines and take a long time to cluster data.
Innovation Solution
A distributed system where training data is split across multiple hosts, each clustering its own subset independently without inter-node communication, allowing for localized evaluation and use of different clustering algorithms, and new data is labeled based on association with existing clusters using a maximum associative factor.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single node approach is used for data clustering, then the system can evaluate the entire dataset together with full data point interactions, but the system requires more powerful machines (higher cost) and takes longer to process large datasets
Solution Approach 1:
The patent divides the entire dataset into multiple subsets and distributes them across multiple nodes for parallel processing. Each node independently clusters its assigned subset, enabling simultaneous processing of multiple data portions without requiring a single powerful machine, thus improving processing speed while maintaining clustering capability
Solution Approach 2:
The patent transitions from a single-node sequential processing approach to a multi-node distributed parallel processing architecture. By adding the spatial dimension of multiple processing nodes, the system achieves faster processing of large datasets while preserving the essential clustering functionality through independent subset evaluation
2Device complexity
If a single node approach is used for data clustering, then the system can maintain simpler architecture, but the system becomes less scalable and requires more expensive powerful machines to handle large datasets
Solution Approach 1:
The system segments the clustering task across multiple independent nodes, each handling a subset of data. This segmentation enables linear scalability where additional nodes can be added to handle larger datasets without requiring upgrades to individual node power, thus improving adaptability while keeping each node relatively simple
Solution Approach 2:
Each node in the distributed system is designed to perform the same clustering function independently on its assigned subset. This universal design allows any node to be added or removed based on processing needs, enhancing scalability while maintaining architectural simplicity through standardized node functionality
3Productivity
If multiple nodes are used for distributed data clustering, then the system improves scalability and processing speed, but the system requires coordination and communication between nodes which increases complexity
Solution Approach 1:
The patent extracts the coordination and communication requirements from the core clustering functionality. Each node performs independent clustering on its subset without needing to communicate with other nodes during the clustering process itself, reducing the complexity of inter-node coordination while maintaining the benefits of distributed parallel processing
Data Source
AI summary
Techniques for classifying and labeling data are disclosed. In one embodiment, the techniques may be realized as a system for classifying and labeling data comprising one or more processors. The one or more processors may be configured to distribute training data across a plurality of hosts. Each of the hosts may be assigned a random subset of the training data, and configured to cluster its own subset independently. The one or more processors may be further configured to label each cluster of the training data. The one or more processors may be further configured to receive new data, associate the new data with a plurality of the clusters of the training data, and assign the new data a label. The label may be chosen from labels of the plurality of the clusters. And the label may have a maximum associative factor of the new data.