Diagnosing performance violations is one of the biggest challenges in transaction level modeling of systems. In this paper, we propose a methodology to localize root causes of latency or throughput violations. We present a concurrent pattern mining approach to infer frequent patterns from transaction traces to localize root causes. We apply three categories of domain knowledge from the violation and models to filter the irrelevant transaction traces and increase the effectiveness of the mining results. We provide three culprit scenarios to mining algorithm by including transaction traces relevant to the corresponding culprit scenario. The mined concurrent patterns then belong to that culprit scenario. We provide a case study for diagnosing performance violations of an experimental platform and show that our domain knowledge can reduce the number of transaction traces by up to 92.8%. The concurrent pattern mining pinpoints the root cause to one of fewer than 10 patterns among 100000 transaction traces.