Introduction and Literature Review We live in a world where data are increasingly becoming available in larger quanti­ ties than ever before and increasingly form the basis of decisions in daily activities. To cope with the ever"increasing volume of data, it is imperative to organize data effectively and efficiently. The very first step of any data organization activity is to classify data. The present thesis emphasizes three aspects of the classification of data. First, it takes a data mining approach as opposed to an empirical or inferen­ trial approach. Second, it restricts the discussion only to sequence data, including multidimensional and time-series data. Third and final, it focuses mainly on un­ supervised classification, that is clustering.