Skeleton Data Point Clustering for Dynamic Distributions
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Solution Overview
Problem
Existing online clustering techniques are inefficient for real-time applications, such as email spam filtering, as they require multiple passes over data points and struggle with dynamically changing data distributions, leading to performance issues and inability to handle high volumes of data effectively.
Innovation Solution
The system maintains skeleton data points for each cluster, represented by a tuple of randomly valued points, allowing new data points to be assigned to clusters based on proximity, enabling fast online clustering without multiple passes and adapting to changing distributions by generating updated clusters and adding new ones as needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If online clustering techniques are used for real-time applications, then processing speed is improved, but the ability to handle dynamically changing data distributions deteriorates
Solution Approach 1:
The patent implements dynamic cluster representation by maintaining multiple skeleton data points for each cluster instead of a single static centroid. This allows the cluster representation to adapt dynamically to changing data distributions while maintaining fast processing speeds. The system can add or remove skeleton points as data arrives, enabling the cluster structure to evolve with the data without requiring multiple passes.
Solution Approach 2:
The patent segments each cluster into multiple skeleton data points that represent different regions or aspects of the cluster. This segmentation allows the system to capture complex, non-convex cluster shapes and adapt to changing distributions more effectively than a single centroid approach, while still maintaining online processing efficiency.
2Measurement precision
If multiple passes over data points are performed, then clustering accuracy is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary action by maintaining pre-computed skeleton data points that capture the essential structure of clusters. When new data arrives, the system only needs to compare against these pre-maintained skeletons rather than reprocessing all data, achieving both accuracy and efficiency in a single pass.
Solution Approach 2:
The patent uses skeleton data points as simplified copies or representations of the actual cluster data. These skeletons capture the essential geometric properties and allow for fast comparison and assignment decisions without requiring access to or reprocessing of the full cluster membership, enabling accurate clustering in one pass.
3Adaptability or versatility
If traditional clustering methods are used, then handling of arbitrary cluster shapes is improved, but processing efficiency deteriorates
Solution Approach 1:
The patent segments clusters into multiple skeleton data points distributed throughout the cluster space, allowing representation of arbitrary and non-convex shapes. Each skeleton point contributes to defining the cluster geometry, enabling the system to handle complex shapes efficiently without requiring multiple passes or sophisticated distance computations.
Solution Approach 2:
The patent transitions from representing clusters by a single centroid (zero-dimensional) to multiple skeleton points that capture the spatial extent and shape (effectively adding dimensional information). This allows the system to represent arbitrary cluster shapes while maintaining the computational simplicity of point-based comparisons.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for clustering data points. One of the methods includes maintaining data representing a respective ordered tuple of skeleton data points for each of a plurality of clusters. One or more intersecting clusters are determined for a new data point. An updated tuple of skeleton data points is generated for an updated cluster by selecting updated skeleton data points, including selecting the new data point or an existing jth skeleton data point of one of the one or more intersecting clusters according to which random value, of the jth random value for the new data point or the random value for the jth existing skeleton data point, is closest to a limiting value. The new data point is then assigned to the updated cluster.