Hierarchical Clustering Taxonomy for Document Organization
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Solution Overview
Problem
Organizations face challenges in managing large volumes of electronic documents due to the inefficiency and poor results of existing clustering algorithms, which fail to effectively organize documents into hierarchical clusters related to specific topics.
Innovation Solution
A computer system that generates a hierarchy of clusters using successive applications of clustering algorithms, with a label manager determining human-readable labels for each cluster, and a taxonomy manager producing a taxonomy based on these labels for organizing and understanding document topics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional clustering algorithms are used to organize large volumes of electronic documents, then documents can be grouped into clusters, but the clustering process is very time consuming and provides poor results with many unrelated documents in each cluster
Solution Approach 1:
The patent segments the large corpus of documents into smaller, manageable subsets that can be clustered independently and in parallel. This segmentation approach reduces the computational burden on single clustering operations, thereby decreasing execution time while maintaining or improving clustering accuracy through focused, specialized processing of smaller document sets.
Solution Approach 2:
The patent introduces a hierarchical dimension to the clustering process by organizing clusters into a taxonomy structure with multiple levels. This dimensional transformation allows the system to manage large volumes of documents efficiently by creating a multi-level hierarchy where top-level clusters represent broad categories and lower-level clusters provide more specific groupings, thus improving both accuracy and scalability.
2Ease of operation
If clustering algorithms are applied to large volumes of documents, then documents can be organized into groups, but the results often contain many unrelated documents making the clusters difficult to manage
Solution Approach 1:
The patent divides the document corpus into smaller subsets for clustering, which results in more homogeneous and coherent clusters. This segmentation ensures that each cluster contains closely related documents, improving the quality of organization and reducing the complexity of managing diverse, unrelated documents within single clusters.
Solution Approach 2:
The patent creates a hierarchical taxonomy structure that organizes clusters across multiple levels. This dimensional approach allows documents to be organized into broad categories at higher levels and more specific groups at lower levels, making the overall system easier to navigate and manage while maintaining high document coherence within each cluster.
3Loss of information
If a detailed hierarchical taxonomy of customer product issues is generated, then companies can understand and address customer feedback effectively, but the process requires sophisticated clustering and labeling systems
Solution Approach 1:
The patent implements a hierarchical taxonomy structure with multiple levels that organizes customer product issues from general to specific. This dimensional hierarchy enables comprehensive capture and understanding of customer feedback nuances while distributing the complexity across multiple manageable levels rather than requiring a single complex classification system.
Solution Approach 2:
The patent employs automated labeling systems that use machine learning and natural language processing to automatically generate labels for clusters. This self-service approach reduces the need for manual taxonomy construction and maintenance, thereby preserving detailed customer feedback information while minimizing the operational complexity of managing the taxonomy system.
Data Source
AI summary
Methods and systems for constructing a taxonomy based on hierarchical clustering are provided. The taxonomy is generated by first constructing a hierarchy of clusters using a clustering algorithm. A first level of the hierarchy of clusters is generated by providing a plurality of content files to a clustering algorithm. Subsequent levels of the hierarchy are generated by providing the clusters of the preceding levels to the clustering algorithm. Labels that characterize each cluster within the hierarchy are assigned to corresponding clusters. Labels and clusters are combined to form the taxonomy.