Association mining extracts common relationships among a finite number of categorical data objects in a set of transactions. However, if the data objects are not categorical and potentially unlimited, it is impossible to employ the association mining approach. On the other hand, clustering is suitable for modeling a large number of non-categorical data objects as long as there exists a distance measure among them. Although it has been used to classify data objects in a data set into groups of similar objects based on data similarity, it can be used to extract the properties of similar data objects commonly appearing in a set of transactions. In this paper, a new clustering method, CLOCK, is proposed to find common knowledge such as frequent ranges of similar objects in a set of transactions. The common knowledge of data objects in the transactions can be represented by the occurrence frequency of similar data objects in terms of a transaction as well as the common repetitive ratio of similar data objects in each transaction. Furthermore, the proposed method also addresses how to maintain identified common knowledge as a summarized profile. As a result, any data difference between a newly collected transaction and the common knowledge of past transactions can be easily identified.