Heterogeneous Transfer Learning in Ensemble Clustering
This work addresses clustering challenges in heterogeneous data domains, but it appears incremental as it builds on existing ensemble and transfer learning approaches.
The paper tackles the problem of ensemble clustering when labeled data from similar but differently-featured datasets are available, proposing a transfer learning method based on meta-features that describe structural characteristics, and experimental results confirm its efficiency with smaller complexity compared to other methods.
This work proposes an ensemble clustering method using transfer learning approach. We consider a clustering problem, in which in addition to data under consideration, "similar" labeled data are available. The datasets can be described with different features. The method is based on constructing meta-features which describe structural characteristics of data, and their transfer from source to target domain. An experimental study of the method using Monte Carlo modeling has confirmed its efficiency. In comparison with other similar methods, the proposed one is able to work under arbitrary feature descriptions of source and target domains; it has smaller complexity.