An Efficient Iterative Framework for Semi- Supervised Clustering Based Batch Sequential Active Learning Approach

Provided by: The International Journal of Innovative Research in Computer and Communication Engineering
Topic: Data Management
Format: PDF
Semi-supervised is the machine learning field. In the previous paper, selection of pairwise constraints for semi-supervised clustering is resolved using active learning method in an iterative manner. Semi-supervised clustering derived from the pairwise constraints. The pairwise constraint depends on the two kinds of constraints such as must-link and cannot-link. In this system, enhanced iterative framework with naive batch sequential active learning approach is applied to improve the clustering performance. The iterative framework requires repeated re-clustering of the data with an incrementally growing constraint set.

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