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.
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