Heavy-Hitters (HHs) are large-volume flows that consume considerably more network resources than other flows combined. In SDN-based DCNs (SDDCNs), HHs cause non-trivial delays for small-volume flows known as non-HHs that are delay-sensitive. Uncontrolled forwarding of HHs leads to network congestion and overall network performance degradation. A pivotal task for controlling HHs is their identification. The existing methods to identify HHs are threshold-based. However, such methods lack a smart system that efficiently identifies HH according to the network behaviour. In this paper, we introduce a novel approach to overcome this lack and investigate the feasibility of using Knowledge-Defined Networking (KDN) in HH identification. KDN by using Machine Learning (ML), allows integrating behavioural models to detect patterns, like HHs, in SDN traffic. Our KDN-based approach includes mainly three modules: HH Data Acquisition Module (HH-DAM), Data ANalyser Module (HH-DANM), and APplication Module (HH-APM). In HH-DAM, we present the flowRecorder tool for organizing packets into flows records. In HH-DANM, we perform a cluster-based analysis to determine an optimal threshold for separating HHs and non-HHs. Finally, in HH-APM, we propose the use of MiceDCER for routing non-HHs efficiently. The per-module evaluation results corroborate the usefulness and feasibility of our approach for identifying HHs.