 |
B. Active learning. Active learning is a sub-field of machine learning topic. The aim of active learning is to develop algorithms, that can learn new feedback at each step in a question-answering process. In the context of semi-supervised clustering, active learning aims to select the most useful constraints, so that they not only boost the clustering performance, but also minimize the number of questions to the user/expert. For active learning methods, we assume that the users/experts are always ready, for answering the question proposed by active learning process. For example, Basu et al, introduced a method for collecting constraints, in which at each step, the algorithm will propose a question, about the relating between two points, and the users will answer the pair of point is, must-link or cannot-link; an active learning method applied for K-means clustering has presented, the idea is using K-means to partition data set into large number of clusters, after that queries will be asked for each pair of clusters, and the process will finish, when users have been satisfied about the final results. |
IEEE/ICACT20220068 Slide.09
[Big Slide]