Probabilistic Clustering with Positive and Negative Constraints
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Solution Overview
Problem
Existing clustering methods fail to perform probabilistic clustering when nodes are classified into multiple clusters, as they cannot effectively handle both positive and negative constraints, limiting the accuracy and applicability of the clustering process.
Innovation Solution
An information processing apparatus with an acquisition unit, calculation unit, and generation unit that acquires network information and constraint data, calculates classification proportions and importance, and generates a probability model using network information, constraint information, classification proportion, and importance to enable probabilistic clustering across multiple clusters, incorporating both positive and negative constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing clustering methods are used to classify nodes into multiple clusters, then nodes can be grouped into different clusters, but probabilistic clustering cannot be performed when nodes are classified as different clusters
Solution Approach 1:
The patent introduces a probability parameter P(n|k) to represent the probability that node n belongs to cluster k. By changing from deterministic classification to probabilistic classification with continuous parameters, the system enables probabilistic clustering while handling multiple clusters. The probability model allows nodes to have membership probabilities across multiple clusters simultaneously, resolving the contradiction between multi-cluster classification and probabilistic clustering capability.
2Ease of manufacture
If constraint information is not incorporated into the clustering process, then the clustering algorithm is simpler to implement, but the clustering accuracy and applicability are limited
Solution Approach 1:
The patent introduces constraint information as an intermediary element that mediates between the clustering algorithm and the desired clustering outcomes. The constraint information (positive constraints indicating nodes that should be in the same cluster, and negative constraints indicating nodes that should be in different clusters) acts as a mediator to guide the probability model learning process. This intermediary mechanism improves clustering accuracy without fundamentally changing the algorithm structure, as the constraints are incorporated into the probability calculation framework.
3Device complexity
If only positive constraints are used in clustering, then the clustering process is simpler, but the ability to enforce negative constraints and improve clustering precision is reduced
Solution Approach 1:
The patent implements negative constraints as counterbalancing forces against positive constraints. The probability model incorporates both positive constraints (which increase the probability of nodes being in the same cluster) and negative constraints (which decrease the probability). These opposing constraint types work together like counterweights, with negative constraints providing the necessary balance to prevent over-clustering and improve classification precision. The dual-constraint approach maintains manageable complexity while significantly enhancing clustering precision.
Data Source
AI summary
An information processing apparatus includes an acquisition unit, a calculation unit, and a generation unit. The acquisition unit acquires information including information regarding multiple nodes and information regarding multiple links connecting the multiple nodes and acquires constraint information regarding node pairs included in the multiple nodes. The constraint information includes a positive constraint and a negative constraint. The calculation unit calculates, for each of multiple clusters, a classification proportion into which the multiple nodes are classified and calculates a degree of importance of each of the multiple clusters. The classification proportion represents a proportion in which each of the multiple nodes is classified as one of the multiple clusters. The generation unit generates a probability model for performing probabilistic clustering on the multiple nodes. The probability model is generated by using at least each of the information regarding the links, the constraint information, the classification proportion, and the degree of importance.