Mining high utility pattern with negative items in dynamic databases

Abstract

High utility pattern mining with negative items (HUPMN) has more practical applications because it can process data with negative utility values. But the existing HUPMN algorithms assume that the database is static and it would be very expensive to use these algorithms directly to deal with dynamic databases. To cope with this challenge, an high utility pattern algorithm for mining negative items from an incremental database is proposed for the first time, and an incremental index list structure is designed, which uses index values to quickly access and update the information stored in the list. In addition, A memory reuse strategy is also applied to reduce memory usage. Finally, a HUPMN algorithm based on sliding window was proposed, which can quickly update item information when the window was sliding. Extensive experimental evaluations have been carried out on a variety of data sets, and the results show that the proposed algorithm exhibits excellent performance in terms of running time and memory usage.

CRFL: A novel federated learning scheme of client reputation assessment via local model inversion

“…Then the server aggregates the local models to update the global model and sends it to the clients for the next round of training. Although the risk of data leak is mitigated, the vanilla FL suffers from the heterogeneity of the client data sets [14][15][16][17] and exhibits unfavorable performance on model accuracy and convergence rate. As stated by Chen et al, 11 Kairouz et al, 18 Zhang et al, 19 Wang et al, 20 and Chen et al, 21 (1) the local data sets cannot represent the overall data distribution, and the local distributions may be also different from each other; (2) the number of clients (e.g., mobile phone users) can be very large, and a large portion of clients are often offline or on unreliable connections; and (3) the amount of data on different clients may be highly imbalanced.…”