Federated Multi-View K-Means Clustering

• Published in: IEEE transactions on pattern analysis and machine intelligence 2024-12, p.1-15
• Main Authors: Yang, Miin-Shen, Sinaga, Kristina P.
• Format: Article
• Language: English
• Subjects: Clustering; Clustering algorithms; Data models; Data privacy; Distributed databases; Federated learning; Federated MVKM; Internet of Things; K-means; Kernel; Machine learning algorithms; Multi-view data; Multi-view k-means (MVKM); Privacy; Servers; Training data
• ISSN: 0162-8828
• DOI: 10.1109/TPAMI.2024.3520708

The increasing effect of Internet of Things (IoT) unlocks the massive volume of the availability of Big Data in many fields. Generally, these Big Data may be in a non-independently and identically distributed fashion (non-IID). In this paper, we have contributions in such a way enable multi-view k-means (MVKM) clustering to maintain the privacy of each database by allowing MVKM to be operated on the local principle of clients' multi-view data. This work integrates the exponential distance to transform the weighted Euclidean distance on MVKM so that it can make full use of development in federated learning via the MVKM clustering algorithm. The proposed algorithm, called a federated MVKM (Fed-MVKM), can provide a whole new level adding a lot of new ideas to produce a much better output. The proposed Fed-MVKM is highly suitable for clustering large data sets. To demonstrate its efficient and applicable, we implement a synthetic and six real multi-view data sets and then perform Federated Peter-Clark in Huang et al. [1] for causal inference setting to split the data instances over multiple clients, efficiently. The results show that shared-models based local cluster centers with data-driven in the federated environment can generate a satisfying final pattern of one multi-view data that simultaneously improve the clustering performance of (non-federated) MVKM clustering algorithms.