Data items arranged into groups form partitions, and across time or through variation of grouping criteria, those partitions may change. While alluvial diagrams, showing the flow of data items as streams, visually capture such changes in partition sequences, their focus on showing similarities between neighboring partitions limits their application. Our paper introduces novel augmentations of alluvial diagrams with interactive visualizations and linked analysis, explicitly targeting the comparison of non-neighboring partitions without sacrificing the sequential nature of the data. Juxtaposed visualizations with the alluvial diagram's timeline provide a comparison of a selected partition to all other partitions, while additional scatterplot views provide an overview of the partition and set similarities. Connecting the set representations across views, we propose a coloring approach of sets and interactive selection mechanisms. The usefulness and generalizability of the approach are demonstrated through examples with application in supervised and unsupervised machine learning, as well as work collaboration analysis.