Text Clustering via Dimensionality Reduction of Multi-Dimensional Arrays
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Solution Overview
Problem
The abundance of user-generated content on the internet poses a challenge in analyzing large amounts of unstructured text data from various sources, such as blog entries, social media posts, and product reviews, as existing tools lack effective methods to extract meaningful insights from this data.
Innovation Solution
The method involves tokenizing n-grams from text units to create a multi-dimensional array, reducing dimensionality through normalization and non-linear dimensionality reduction, and clustering text units to visualize concepts, allowing for the identification of patterns and themes within the data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional text analysis tools are used on user-generated content, then analysis can be performed, but the tools cannot effectively extract meaningful insights from large amounts of unstructured text data
Solution Approach 1:
The patent segments text data into discrete units (tokens, n-grams) and represents them as vectors in a multi-dimensional space. This segmentation allows complex unstructured text to be broken down into manageable components that can be individually analyzed and recombined to extract meaningful insights while reducing overall analysis complexity.
Solution Approach 2:
The patent transforms text data from one-dimensional sequential characters into multi-dimensional vector representations. By mapping text units to points in a multi-dimensional space where dimensions represent semantic features, the system enables effective analysis and insight extraction that would be impossible with traditional linear text processing methods.
2Loss of information
If large amounts of text data are analyzed in detail, then meaningful insights can be extracted, but the computational complexity and processing time increase significantly
Solution Approach 1:
The patent changes the parameters of text representation from raw character sequences to normalized vector representations with controlled dimensionality. By adjusting the number of dimensions and applying normalization, the system maintains sufficient detail for insight extraction while reducing computational complexity and processing time through optimized parameter selection.
3Measurement precision
If high-dimensional text data is processed, then detailed analysis is possible, but the data complexity and computational requirements become unmanageable
Solution Approach 1:
The patent strategically transforms high-dimensional text data into an optimized multi-dimensional vector space where the number of dimensions is balanced to preserve analytical precision while reducing computational complexity. This dimensional transformation enables detailed analysis without overwhelming computational requirements.
Solution Approach 2:
The patent adjusts dimensional parameters and applies normalization to transform high-dimensional raw text data into optimized vector representations. By carefully selecting and adjusting dimensional parameters, the system maintains measurement precision for accurate text analysis while reducing overall data complexity to manageable levels.
Data Source
AI summary
Methods, systems, and apparatuses, including computer programs encoded on computer-readable media, for tokenizing n-grams from a plurality of text units. A multi-dimensional array is created having a plurality of dimensions based upon the plurality of text units and the n-grams from the plurality of text units. The multi-dimensional array is normalized and the dimensionality of the multi-dimensional array is reduced. The reduced dimensionality multi-dimensional array is clustered to generate a plurality of clusters that each cluster includes one or more of the plurality of text units.