With wafer fabs running at near full capacity, it is a constant challenge to maintain high yields. Many different products are fabricated by the same equipment. So the sudden change in product yield, a yield excursion, can have a significant impact to many different products. Therefore, it is critical to detect an excursion as early as possible and fix the cause in order to minimize the impact. This paper introduces a two-part methodology for excursion detection and quality improvement. This methodology is based on a novel method called Kernel Based Clustering. First, a screening method will be described for removing die in close proximity to the cluster of failing dies. Second, a cluster commonality methodology will be described for detecting common clusters in terms of shape, region on the wafer and failure mode. This methodology was evaluated with 40k wafers from a 30-week production period. These wafers came from 15 different products developed on the same technology. During this period, a process excursion occurred that impacted many of these products. It will be shown that the kernel based clustering algorithm effectively identifies and removes high-risk dies around the failure cluster. It will also be shown that common clusters can be identified across multiple products and with this capability, the time to detection can be reduced.