Cluster Kinematics for Temporal Data Movement Analysis
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Solution Overview
Problem
Conventional clustering approaches fail to account for temporal changes in samples associated with a same object, leading to inadequate identification of movement in the clustering model's projection space, which affects the accuracy of cluster assignments over time.
Innovation Solution
Implement cluster kinematics by calculating kinematic metrics such as velocity and acceleration based on temporal sequences of data samples, projecting them into a clustering model's projection space, and determining convergence or divergence from cluster centers, allowing for more accurate and earlier prediction of future cluster assignments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional clustering approaches are used to group data samples, then cluster assignments can be obtained, but temporal changes in samples associated with the same object cannot be identified
Solution Approach 1:
The patent applies dynamics by transitioning from static cluster assignment to dynamic trajectory analysis. Instead of merely assigning samples to clusters, the system calculates velocities and accelerations of sample trajectories in the projection space, enabling identification of temporal changes and movement patterns associated with the same object over time.
Solution Approach 2:
The patent adds temporal dimension to the traditional spatial clustering by projecting samples into a higher-dimensional space that includes both feature space and time. This allows simultaneous analysis of spatial cluster assignments and temporal movement patterns, resolving the contradiction between clustering accuracy and temporal information preservation.
2Productivity
If samples are processed in isolation without considering temporal sequences, then computational complexity is reduced, but the ability to predict future cluster assignments is compromised
Solution Approach 1:
The patent performs preliminary calculations of velocities and accelerations using recent historical data points, which enables prediction of future cluster assignments. By pre-computing kinematic metrics from the most recent temporal sequence, the system can forecast future states without requiring complex real-time analysis of all historical data.
Solution Approach 2:
The system uses feedback from recent temporal sequences of samples to continuously update trajectory predictions. By analyzing the most recent velocities and accelerations, the system adjusts future predictions dynamically, improving predictive accuracy while maintaining efficient processing through focus on recent relevant data rather than all historical data.
3Device complexity
If only static cluster centers are used for classification, then the clustering model is simple, but it cannot identify movement or convergence trends in projection space
Solution Approach 1:
The patent extends the static clustering model by adding dynamic kinematic components. Instead of using only fixed cluster centers, the system calculates velocities and accelerations of sample trajectories, enabling detection of movement and convergence trends. This dynamic extension allows the model to identify temporal patterns while maintaining the simplicity of the underlying clustering structure.
Data Source
AI summary
Methods, systems, and computer-readable storage media for using cluster kinematics for enhanced data analysis of temporal data. A data sample from a temporal sequence of data samples associated with a node is received and projected into a projection space of a clustering model defining one or more clusters. A distance value associated with each of the one or more clusters in the clustering model is calculated for the data sample. One or kinematic metrics associated with a cluster in the clustering model are calculated for the node from the calculated distance values, a time value associated with the received data sample, and previously calculated distance values and kinematic metrics for the node calculated from previously received data samples, where the calculated kinematic metrics represent a trajectory of the node in relation to the cluster in the projection space of the clustering model.