Searching information or specific knowledge to understand decisions in a huge amount of data can be a difficult task. To support this task, classification is one of several used strategies. Algorithms used to support the process of auto-mated classification leads to large and often noisy classes that is difficult to interpret. In this paper we present a method that exploits the notion of association rules and maximal association rules, in order to seek strong lexical associations in classes of similarities. We will show in experimentation section how these lexical associations can assist in understanding owner-managers decisions.
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