scholarly journals A rough set based algorithm for updating the modes in categorical clustering

2021 ◽  
Author(s):  
Semeh Ben Salem ◽  
Sami Naouali ◽  
Zied Chtourou
Keyword(s):  
Rough Set ◽  
Categorical Clustering

scholarly journals An Empirical Analysis of Rough Set Categorical Clustering Techniques

PLoS ONE ◽  
2017 ◽  
Vol 12 (1) ◽  
pp. e0164803 ◽  
Author(s):  
Jamal Uddin ◽  
Rozaida Ghazali ◽  
Mustafa Mat Deris
Keyword(s):  
Empirical Analysis ◽  
Rough Set ◽  
Clustering Techniques ◽  
Categorical Clustering

Clustering Categorical Data: A Survey

2020 ◽  
Vol 19 (01) ◽  
pp. 49-96
Author(s):  
Sami Naouali ◽  
Semeh Ben Salem ◽  
Zied Chtourou
Keyword(s):  
Rough Set ◽  
Categorical Data ◽  
High Efficiency ◽  
Clustering Algorithms ◽  
Low Complexity ◽  
Clustering Methods ◽  
Partitional Clustering ◽  
Fuzzy Methods ◽  
Real World Applications ◽  
Categorical Clustering

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.

THE (DOMINANCE BASED) ROUGH SET APPROACH APPLIED TO AIR POLLUTION IN A HIGH RISK RATE INDUSTRIAL AREA

2018 ◽  
Vol 17 (3) ◽  
pp. 591-599 ◽  
Author(s):  
Agata Matarazzo ◽  
Maria Teresa Clasadonte ◽  
Carlo Ingrao
Keyword(s):  
Air Pollution ◽  
High Risk ◽  
Rough Set ◽  
Industrial Area ◽  
Risk Rate

ANALISA DATA EKSPEDISI PAKET DENGAN MENERAPKAN ALGORITMA ROUGH SET (STUDI KASUS: JNE AGEN MENTENG MEDAN)

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Hasrat Selpia Simorangkir
Keyword(s):  
Data Mining ◽  
Rough Set ◽  
Microsoft Excel ◽  
General Rules ◽  
New Knowledge ◽  
Analyze Data

In analyzing data with data mining using the Rough Set algorithm which has results in the form of rules (rules). The process of determining the rules in the Rough Set method starts from processing the Microsoft Excel database, and continues testing with the Rough Set method. Thus producing General Rules which will become new knowledge in this research.With the existence of data mining using the rough set penilus algorithm, it can analyze data and provide solutions to the weaknesses faced in the menteng Medan field agents that have occurred so that it can be used as an alternative to solving problems in packet expedition encountered so far.Keyword: Data Mining, Descision System, Equivalen Class, ,General Rules, Rough set,Reduction

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