In this study, we use the concepts of the cover coefficient-based clustering methodology (C³M) for on-line new event detection and event clustering.  The main idea of the study is to use the seed selection process of the C³M algorithm for the purpose of detecting new events.  Since C³M works in a retrospective manner, we modify the algorithm to work in an on-line environment.  Furthermore, in order to prevent producing oversized event clusters, and to give equal chance to all documents to be the seed of a new event, we employ the window size concept.  Since we desire to control the number of seed documents, we introduce a threshold concept to the event clustering algorithm.  We also use the threshold concept, with a little modification, in the on-line event detection.  In the experiments we use TDT1 corpus, which is also used in the original topic detection and tracking study.  In event clustering, we use both binary and weighted versions of TDT1 corpus.  With the binary implementation, we obtain better results.  When we compare our on-line event detection results to the results of UMASS approach, we obtain 53% better performance in terms of false alarm rates.