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This slide summarizes our proposed work.
We propose a new active learning method for collecting constraints from users/experts. These collected constraints are applied for enhancing the results of semi- supervised clustering algorithms. The main idea of our new method is based on density peak estimation and min-max strategy. The experiments conducted on some UCI data sets show the effectiveness of our method.
The detail step of our algorithm is presented in Algorithm 1.
Given a data set X with n points, the key idea of our algorithm is to apply density peaks estimation method and min-max method for building an active learning strategy. Firstly, we calculate local density and for every points of data and construct a decision graph. Secondly, we choose the peaks based on the decision graph, the set of chosen peaks are as an initial skeleton. Thirdly, we build an active learning process in which at each step, a new point is chosen following the min- max method using the skeleton and the point is used in forming user questions for getting label from users. |
IEEE/ICACT20220071 Slide.10
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