K-Means Clustering for Platform Data Filtering
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Solution Overview
Problem
Current communication systems, such as collaboration platforms, are inefficient in filtering and classifying large volumes of data, making it time-consuming for users to find relevant information, as traditional keyword searches often yield low-quality and irrelevant results.
Innovation Solution
A method using machine learning algorithms, specifically k-means clustering, to cluster user queries and platform data, identify corresponding domains, rank data relevance, and route high-ranked data to users, improving data filtering and classification efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional keyword search is used to search platform data, then the search process is simple and fast, but the search results have low quality and relevance
Solution Approach 1:
The patent introduces k-means clustering as an intermediary mechanism between the user query and the platform data. The system clusters both the query and platform data into groups, then matches clusters rather than individual items. This intermediary clustering process improves result quality by grouping semantically related items together, while keeping the overall system architecture relatively simple and modular.
2Productivity
If manual or traditional search methods are used to process large volumes of platform data, then the system requires minimal processing complexity, but users spend excessive time sorting through irrelevant data
Solution Approach 1:
The patent applies preliminary action by pre-clustering the platform data using k-means clustering before the actual search query is processed. The data is organized into clusters and domains in advance, so when a user submits a query, the system can quickly match the query cluster against pre-existing data clusters. This preliminary organization dramatically reduces the time users spend sorting through irrelevant data, as the system has already done the heavy lifting of categorization beforehand.
3Ease of operation
If all platform data is returned to users without filtering, then the system maintains simplicity in the retrieval process, but users are overwhelmed by large volumes of irrelevant information
Solution Approach 1:
The patent implements local quality by applying different processing treatments to different portions of the platform data based on their cluster characteristics. Rather than uniformly processing all data, the system identifies high-ranked clusters that are most relevant to the query and prioritizes those results. This allows the system to maintain operational simplicity for the user while applying intelligent filtering and ranking to improve information relevance, effectively delivering high-quality results without overwhelming the user.
Data Source
AI summary
Systems and methods are provided for classifying and filtering data, including receiving a query via a platform, the platform including platform data; clustering the query to create a query cluster; identifying a domain of the query cluster; clustering the platform data to create platform data clusters; identifying a corresponding platform data cluster, the corresponding platform data cluster being one of the platform data clusters that is in a same domain as the domain of the query cluster; assigning a rank to one or more portions of the platform data within the corresponding platform data cluster; and routing at least one of the portions of the platform data having a rank greater than a threshold.