  Cluster analysis is one of the essential tasks in data mining and knowledge
discovery. Each type of data poses unique challenges in achieving relatively
efficient partitioning of the data into homogeneous groups. While the
algorithms for numeric data are relatively well studied in the literature,
there are still challenges to address in case of categorical data. The main
issue is the unordered structure of categorical data, which makes the
implementation of the standard concepts of clustering algorithms difficult. For
instance, the assessment of distance between objects, the selection of
representatives for categorical data is not as straightforward as for
continuous data. Therefore, this paper presents a new framework for
partitioning categorical data, which does not use the distance measure as a key
concept. The Matching based clustering algorithm is designed based on the
similarity matrix and a framework for updating the latter using the feature
importance criteria. The experimental results show this algorithm can serve as
an alternative to existing ones and can be an efficient knowledge discovery
tool.
