In this study, we develop an innovative federated framework for erasable itemset mining to address the challenges of horizontal federated learning in data mining and resolve the shortcomings of the previous algorithm. The framework is based on a client-server architecture, in which clients from multiple data sources collaborate to effectively integrate information. The proposed algorithm is divided into two parts, including the client-side mining and the server-side aggregation. During the client-side mining stage, the algorithm introduces quasi-erasable itemsets to collect additional useful information, facilitating the integration of results on the server side. In the server-side aggregation stage, the algorithm employs a boundary strategy, effectively utilizing the clients' quasi-erasable and erasable itemsets to improve the accuracy of the results. Experimental results demonstrate that the proposed method not only effectively mines complete knowledge but also ensures data-privacy protection.

In this paper, we try to automatically induce the membership functions appropriate for the TS fuzzy model. A GA-based learning algorithm is thus proposed to achieve the purpose. The proposed approach considers the shapes of membership functions in fitness evaluation in addition to the accuracy. The shapes of membership functions are evaluated by the overlap and coverage factors, which are used to avoid the bad types of membership functions. The experimental results show that the proposed approach can derive the membership functions in the Takagi-Sugeno system with low errors and good shapes.