Automated Document Clustering via Similarity Scoring
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Solution Overview
Problem
Current document clustering systems face challenges such as poor document quality, incorrect template identification, and lack of automation, leading to inaccurate sorting and increased manual intervention, particularly with algorithms like k-means clustering.
Innovation Solution
A system that automatically identifies a template document by scanning for similar objects across multiple documents, generating scores based on occurrences, and using these scores to cluster documents without user intervention, while also correcting errors in the template document for improved clustering accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If k-means clustering algorithm is used with user-defined template and number of clusters, then clustering can be performed, but accuracy decreases and manual intervention increases due to poor document quality and incorrect template identification
Solution Approach 1:
The system automatically selects the template document and determines the number of clusters without user intervention. The template selection is performed by computing similarity scores between documents and identifying the one with the highest score, while the number of clusters is determined through automated analysis of document similarity patterns.
Solution Approach 2:
The system performs preliminary document analysis by computing similarity scores and identifying common objects across documents before clustering. This preliminary action includes scanning documents for similar objects, generating scores based on occurrence frequencies, and selecting the template document in advance of the actual clustering operation.
2Productivity
If user defines template document manually, then clustering process can start, but errors in template selection lead to incorrect clustering and require multiple iterations
Solution Approach 1:
The system computes similarity scores for each document based on the occurrence of common objects and uses this feedback to automatically select the template document. The feedback mechanism continuously evaluates document similarity and adjusts template selection to ensure the most representative document is chosen, thereby improving clustering reliability.
Solution Approach 2:
The system changes the parameter of template selection from manual user choice to automated score-based selection. By computing similarity scores based on object occurrence frequencies and selecting the document with the highest score, the system transforms the template selection process into a quantitative, objective parameter-driven approach.
3Extent of automation
If number of clusters is defined by user, then clustering can be performed, but incorrect cluster definition causes sorting errors and increased manual intervention
Solution Approach 1:
The system automatically determines the number of clusters by analyzing document similarity patterns and score distributions without requiring user input. This self-service approach eliminates manual cluster definition while maintaining sorting accuracy through automated analysis of the document set characteristics.
Data Source
AI summary
A scanner scans a group of documents. For example, the documents can be a group of invoices. The documents are received and processed. Objects (e.g., a text object, such as a word) and their locations are identified in each of the documents. Occurrences of similar objects in the identified locations between the documents are determined. A document sorting algorithm is applied to generate a score for each of the documents. The score for each of the documents is generated based on a number of occurrences of similar objects between the documents. The generated score of each of the documents is used to identify a template document. The template document is then used to cluster the documents.