Resumen de Process Partitions from Time-Ordered Clusters

Peter Harnish, Ben Nelson, George C. Runger

• Standard statistical methods based on global models may not be effective for the analysis of massive multivariate industrial data sets unless the data is partitioned into stable operating regions. Constrained clustering is proposed as a robust solution to detect change points and partition historical process data. The constraint is that only observations that are contiguous in time can be joined. A method is described for partitioning data sets into stable regions by modifying agglomerative clustering algorithms to take into account the time order within the data set. A stopping criterion is proposed to evaluate the number of change points generated.