Clustering Categorical Data: A Survey

Abstract

Clustering is a complex unsupervised method used to group most similar observations of a given dataset within the same cluster. To guarantee high efficiency, the clustering process should ensure high accuracy and low complexity. Many clustering methods were developed in various fields depending on the type of application and the data type considered. Categorical clustering considers segmenting a dataset in which the data are categorical and were widely used in many real-world applications. Thus several methods were developed including hard, fuzzy and rough set-based methods. In this survey, more than 30 categorical clustering algorithms were investigated. These methods were classified into hierarchical and partitional clustering methods and classified in terms of their accuracy, precision and recall to identify the most prominent ones. Experimental results show that rough set-based clustering methods provided better efficiency than hard and fuzzy methods. Besides, methods based on the initialization of the centroids also provided good results.

Categorical Data Clustering: A Bibliometric Analysis and Taxonomy

“…Notably, using only the WoS database is considered sufficient for retrieving clustering-related articles. Additionally, many review papers rely on the official website [17][18][19]24] or the publisher's website [20][21][22] as their primary source. In another study, Wang et al [23] retrieved articles indexed by Google Scholar and the WoS database.…”

“…For instance, ref. [17] categorizes clustering into hard, fuzzy, and rough set clustering. Another taxonomy, proposed by [18], classifies distance or similarity metrics for categorical data, distinguishing similarity into context-sensitive and context-free, with the context-free category comprising probabilistic, information-theoretic, and frequency-based approaches.…”

Gradient-based elephant herding optimization for cluster analysis

“…Clustering algorithms can be broadly classified into two categories, namely, hierarchical and partitional methods [11]. Hierarchical clustering methods assume a hierarchical structure between clusters and recursively find nested clusters.…”

Cluster Analysis of Mixed and Missing Chronic Kidney Disease Data in KwaZulu-Natal Province, South Africa

“…Converting continuous variables into categorical ones generally involves binning into a pre-determined number of representative categories, which then allows for the application of clustering methods developed for categorical data such as CACTUS [7], Squeezer [8], Clarke et al's [9] ensemble method, and a plethora of others [10]. However, this form of discretization results in loss of information and poses a new non-trivial challenge of selecting an appropriate discretization scheme, a critical choice that directly determines the resulting similarity matrix and/or clustering [3].…”