Rare Feature Indexing for Document Clustering Granularity
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Solution Overview
Problem
Current document clustering algorithms tend to create a few large clusters, which broadens the relationships within each cluster, degrading the quality of resource organization and efficiency in indexing and searching.
Innovation Solution
The method involves identifying resources that share rare features, excluding stop words, and using metadata like URLs, titles, and authors, to create clusters by merging resources with similar features based on a similarity score calculated from shared and unique features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current clustering algorithms are used to organize resources, then resources are grouped into clusters, but the clusters become very large and relationships are broadened, degrading the quality of organization
Solution Approach 1:
The patent segments the resource set into multiple smaller clusters based on rare features rather than creating a few large clusters. By dividing the clustering task into finer granularity segments using rare feature identification, each cluster maintains tighter relationships and higher quality organization without becoming overly broad or large.
Solution Approach 2:
The patent applies local quality by focusing on rare features that are specific to particular subsets of resources. Instead of using general features that apply broadly across all resources, the system identifies and utilizes locally distinctive rare features to define cluster boundaries, ensuring that each cluster has high-quality, specific relationships among its members.
2Manufacturing precision
If rare features are used for clustering, then cluster quality improves, but the number of comparisons required increases
Solution Approach 1:
The patent performs preliminary action by pre-identifying and indexing rare features across the resource set before the actual clustering process. By preparing a rare feature index in advance that maps resources to their rare features, the system avoids performing exhaustive comparisons during clustering, significantly reducing the time required while maintaining high cluster quality.
3Measurement precision
If more features are extracted from resources, then clustering accuracy improves, but processing complexity increases
Solution Approach 1:
The patent applies the extraction principle by selectively extracting only rare features from resources rather than processing all possible features. By identifying and extracting the subset of rare features that are most discriminative for clustering, the system achieves high clustering accuracy while avoiding the complexity of processing the complete feature set.
Data Source
AI summary
Systems, methods, and apparatus for clustering resources using rare features are provided. For example, an environment includes an extraction module, an index module, and a cluster module. The extractions module identifies a set of resources and extracts a plurality of features from the resources. The plurality of features may be rare features. The index module identifies and generates a rare features index. The cluster module identifies at least two resources that share rare features, creates one or more clusters based on the identified at least two resources, and associates resources that share similar features with the one or more clusters. Resources that do not share similar features are not associated with the one or more clusters. Identifying at least two resources that share rare features is based at least upon a threshold.