Document Clustering via Time-Sliced Topic Analysis
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Solution Overview
Problem
Conventional topic modeling techniques for document clustering in chat messaging systems often produce imprecise results, leading to the inclusion of irrelevant documents and unnecessary resource consumption, as they fail to accurately identify conversation topics over time.
Innovation Solution
A method that divides documents into time slices and redefines clusters based on patterns of change in topic keywords over time, using overlapping time slices to identify topic convergence and drift, thereby refining clustering for more homogeneous and relevant groups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional topic modeling techniques are used for document clustering, then the clustering process can be performed, but the results are imprecise and produce non-homogeneous groups of documents
Solution Approach 1:
The patent segments the document collection into multiple time slices, analyzing documents chronologically. By dividing the corpus into temporal segments and identifying topic transitions between them, the system achieves more precise clustering that respects the evolutionary nature of topics over time, resolving the imprecision of conventional static topic modeling.
Solution Approach 2:
The patent introduces dynamic analysis by tracking topic keywords across multiple time slices. Instead of treating topics as static, the system identifies how topics evolve, converge, or diverge over time, enabling more accurate and homogeneous document grouping that adapts to temporal changes in topic relevance.
2Productivity
If conventional topic modeling is used to group documents, then clustering can be achieved, but irrelevant documents are included and resource consumption increases
Solution Approach 1:
The patent performs preliminary analysis by identifying topic keywords and their temporal patterns before final document assignment. By pre-processing documents into time slices and establishing topic evolution patterns in advance, the system enables more efficient retrieval with fewer irrelevant documents included, reducing subsequent processing resources required.
3Reliability
If static topic clustering is performed without time analysis, then the process is simple, but it fails to capture topic changes over time
Solution Approach 1:
The patent segments documents into time slices and introduces temporal dimension to topic analysis. This segmentation approach systematically captures topic evolution while maintaining a structured, manageable process that balances accuracy improvement with acceptable complexity through organized temporal processing.
Solution Approach 2:
The patent changes the parameter of time by introducing temporal analysis across multiple time slices. By tracking how topic keywords appear, disappear, or change prominence over time, the system achieves more reliable topic identification while the complexity is managed through systematic parameter tracking rather than complex algorithms.
Data Source
AI summary
A system, method and computer program product performs data analysis and clustering. A plurality of data objects are received, each represented by a vector of features and associated with a point in time. The plurality of data objects is divided into first time slices to form a plurality of consecutive sets of data objects. Each set of data objects is sub-divided into one or more second time slices so as to form one or more subsets of data objects. The data objects in each set and subset of data objects are processed to derive clusters of data objects according to similarity of features. The clusters of data objects from different sets and subsets of data objects are used to detect changes in the relevance of cluster features over time.