Feature Induction and Network Mining with Clustering Tree Ensembles

The volume of data generated and collected using modern technologies grows exponentially. This vast amount of data often follows a complex structure, and the problem of efficiently mining and analyzing such data is crucial for the performance of various machine learning tasks. Here, a novel data mining framework for unsupervised learning tasks is proposed based on decision tree learning and ensembles of trees. The proposed approach introduces an informative feature representation and is able to handle data diversity and complexity. Moreover, a new scheme is proposed based on the aforementioned approach for mining interaction data. These data are often modeled as homogeneous or heterogeneous networks and they are present in various fields, such as social media, recommender systems, and bioinformatics. The learning process is performed in an unsupervised manner, following also the inductive setup. The experimental evaluation confirms the effectiveness of the proposed approach.