Data mining is an attempt to obtain information from a mass of data that is not easily discernable. Since many data mining techniques are designed for discrete data while frequently the data is actually continuous, there is a great need for reasonable approaches for converting the data from continuous to discrete. This thesis will examine one discretization technique and some of its implications. Clustering data is also an important field of study in data mining. This paper will examine a technique for clustering and uses thereof. The unifying topic is a distance function, called the Barthélemy-Monjardet distance, which will be used for both discretizing and clustering
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