Data clustering has attracted a lot of attention in the field of data mining and computational statistics. Many methods have been developed for clustering objects according to their similarity.
However, most of the traditional clustering algorithms that use distances between points for clustering are not appropriate for non-numeric attributes such as nominal, boolean and categorical data. Because the data type of these specific applications is non-numeric(non-metric), thus it is not easy to map attribute tuples to vectors. And sometimes we would lose certain useful information embedded in data attributes with using traditional distance-based clustering algorithms directly.
This thesis proposes and explores two simple algorithms more
suitable than distance-based clustering algorithms for non-numeric data clustering. The Cosine measure for document clustering triggers the idea of our similarity measure. This similarity measure can easily reflect the ordinal information of data variables of sequential data. It means that our similarity measure shows whether the ``quality of consecution" is good or not. In addition, some issues about similarity matrix (SM) partitioning and Union-Find-like methods are discussed in detail.

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