Dense Cluster Filtering for Large-Scale Content Processing
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Solution Overview
Problem
Conventional clustering algorithms are computationally expensive and struggle to identify clusters within large datasets, such as those containing millions of data points, due to their high complexity and cost, leading to inefficiencies in processing and accuracy.
Innovation Solution
A method that employs a preliminary filter using a fast clustering algorithm to identify and remove high-density clusters of uninteresting content items, followed by dimensionality reduction techniques and more rigorous clustering algorithms to analyze the remaining data, allowing for efficient labeling of content items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional clustering algorithms are used on large datasets, then clustering accuracy can be improved, but computational cost and processing time increase exponentially
Solution Approach 1:
The patent segments the large dataset into multiple partitions or subsets that can be processed independently. By dividing the data into smaller manageable pieces, the computational complexity of clustering algorithms is reduced while maintaining clustering accuracy through subsequent merging or sampling of results from different partitions.
Solution Approach 2:
The patent applies preliminary filtering, dimensionality reduction, or data preprocessing steps before executing the main clustering algorithm. This preliminary action reduces the data size and complexity, making the subsequent clustering operation more efficient while preserving the essential structures needed for accurate clustering.
2Reliability
If conventional clustering algorithms are used on large datasets, then comprehensive cluster identification can be achieved, but processing time becomes prohibitively long
Solution Approach 1:
The patent extracts and removes irrelevant features, noise, or unimportant data points from the dataset before applying clustering algorithms. This extraction process reduces the effective data size that needs to be processed, significantly decreasing processing time while maintaining the integrity of meaningful clusters.
Solution Approach 2:
The patent applies clustering algorithms to a carefully selected subset or sample of the data rather than processing the entire dataset. By using partial action on representative samples, the system achieves sufficient cluster identification speed while maintaining acceptable reliability through strategic sampling or iterative refinement.
3Measurement precision
If dataset size is increased to improve AI application accuracy, then more comprehensive data analysis is possible, but processing complexity and costs grow exponentially
Solution Approach 1:
The patent transforms the data by changing parameters such as dimensionality, feature representation, or data format through techniques like dimensionality reduction, feature selection, or encoding. These parameter changes reduce the computational burden of processing large datasets while preserving the information necessary for accurate AI applications.
Solution Approach 2:
The patent creates simplified copies, representations, or proxies of the original large dataset that capture the essential patterns and structures. These copied representations can be processed more efficiently while still providing the accuracy needed for AI applications, avoiding the need to process the full original dataset.
Data Source
AI summary
Systems and methods are described for determining clusters for large collections of content items. A fast cluster-identifying algorithm can be used to find high density areas where certain less interesting content items might be clustered in a feature space. An example algorithm is a mean shift algorithm. Once these high-density clusters are located, a system can remove them and proceed to analyze the remaining data. Removing these clusters of featureless content items can greatly reduce the collection size and also enhance the overall quality of the collection. Labels can then be applied to clusters and, when a content item is received, classification algorithms can be used to assign an appropriate label to the content item.