CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
CACTUS—clustering categorical data using summaries
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Clustering categorical data: an approach based on dynamical systems
The VLDB Journal — The International Journal on Very Large Data Bases
Cluster Analysis for Gene Expression Data: A Survey
IEEE Transactions on Knowledge and Data Engineering
Clicks: An effective algorithm for mining subspace clusters in categorical datasets
Data & Knowledge Engineering
MMR: An algorithm for clustering categorical data using Rough Set Theory
Data & Knowledge Engineering
The normal parameter reduction of soft sets and its algorithm
Computers & Mathematics with Applications
On some new operations in soft set theory
Computers & Mathematics with Applications
“Best K”: critical clustering structures in categorical datasets
Knowledge and Information Systems
A Direct Proof of Every Rough Set is a Soft Set
AMS '09 Proceedings of the 2009 Third Asia International Conference on Modelling & Simulation
The parameterization reduction of soft sets and its applications
Computers & Mathematics with Applications
Constructive and algebraic methods of the theory of rough sets
Information Sciences: an International Journal
G-ANMI: A mutual information based genetic clustering algorithm for categorical data
Knowledge-Based Systems
A rough set approach for selecting clustering attribute
Knowledge-Based Systems
On multi-soft sets construction in information systems
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
A soft set approach for association rules mining
Knowledge-Based Systems
Soft decision making for patients suspected influenza
ICCSA'10 Proceedings of the 2010 international conference on Computational Science and Its Applications - Volume Part III
Cluster analysis of gene expression data based on self-splitting and merging competitive learning
IEEE Transactions on Information Technology in Biomedicine
A novel soft set approach in selecting clustering attribute
Knowledge-Based Systems
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Clustering, which is a set of categorical data into a homogenous class, is a fundamental operation in data mining. One of the techniques of data clustering was performed by introducing a clustering attribute. A number of algorithms have been proposed to address the problem of clustering attribute selection. However, the performance of these algorithms is still an issue due to high computational complexity. This paper proposes a new algorithm called Maximum Attribute Relative (MAR) for clustering attribute selection. It is based on a soft set theory by introducing the concept of the attribute relative in information systems. Based on the experiment on fourteen UCI datasets and a supplier dataset, the proposed algorithm achieved a lower computational time than the three rough set-based algorithms, i.e. TR, MMR, and MDA up to 62%, 64%, and 40% respectively and compared to a soft set-based algorithm, i.e. NSS up to 33%. Furthermore, MAR has a good scalability, i.e. the executing time of the algorithm tends to increase linearly as the number of instances and attributes are increased respectively.