A matching based clustering algorithm for categorical data

Ruben A. Gevorgyan · Y enok B. Hakobyan Abstract 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 numeric 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. Keywords categorical data · clustering algorithm · similarity matrix · feature importance Mathematics Subject Classification (2010) 62H30 · 62H17 · 62H20 Ruben A Gevorgyan Faculty of Economics and Management, Y erevan State University, Alex Manukyan 1, 0025 Y erevan, Republic of Armenia Email: rubengevorgyan@ysu.am Y enok B. Hakobyan Faculty of Economics and Management, Y erevan State University, Alex Manukyan 1, 0025 Y erevan, Republic of Armenia Email: e.hakobyan@ysu.am 1 Introduction Cluster analysis is one of the "super problems"s in data mining. Generally speaking, clustering is partitioning data points into intuitively similar groups (Saxena et al. 2017).