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 OF DECISION TABLES TOWARD ROUGH SET-BASED GROUP DECISION AID

2011 ◽  
Vol 19 (supp01) ◽  
pp. 17-32 ◽  
Author(s):  
MASAHIRO INUIGUCHI ◽  
RYUTA ENOMOTO
Keyword(s):  
Hierarchical Clustering ◽  
Rough Set ◽  
Real World ◽  
Decision Aid ◽  
Group Decision ◽  
Decision Rules ◽  
Agglomerative Hierarchical Clustering ◽  
Clustering Techniques ◽  
Fuzzy C Means ◽  
Decision Tables

In order to analyze the distribution of individual opinions (decision rules) in a group, clustering of decision tables is proposed. An agglomerative hierarchical clustering (AHC) of decision tables has been examined. The result of AHC does not always optimize some criterion. We develop non-hierarchical clustering techniques for decision tables. In order to treat positive and negative evaluations to a common profile, we use a vector of rough membership values to represent individual opinion to a profile. Using rough membership values, we develop a K -means method as well as fuzzy c-means methods for clustering decision tables. We examined the proposed methods in clustering real world decision tables obtained by a questionnaire investigation.

scholarly journals A unified approach for cluster-wise and general noise rejection approaches for k-means clustering

2019 ◽  
Vol 5 ◽  
pp. e238
Author(s):  
Seiki Ubukata
Keyword(s):  
Linear Function ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Unified Approach ◽  
Cluster Assignment ◽  
Clustering Techniques ◽  
Noise Rejection ◽  
Classification Boundary ◽  
Influence Of Noise

Hard C-means (HCM; k-means) is one of the most widely used partitive clustering techniques. However, HCM is strongly affected by noise objects and cannot represent cluster overlap. To reduce the influence of noise objects, objects distant from cluster centers are rejected in some noise rejection approaches including general noise rejection (GNR) and cluster-wise noise rejection (CNR). Generalized rough C-means (GRCM) can deal with positive, negative, and boundary belonging of object to clusters by reference to rough set theory. GRCM realizes cluster overlap by the linear function threshold-based object-cluster assignment. In this study, as a unified approach for GNR and CNR in HCM, we propose linear function threshold-based C-means (LiFTCM) by relaxing GRCM. We show that the linear function threshold-based assignment in LiFTCM includes GNR, CNR, and their combinations as well as rough assignment of GRCM. The classification boundary is visualized so that the characteristics of LiFTCM in various parameter settings are clarified. Numerical experiments demonstrate that the combinations of rough clustering or the combinations of GNR and CNR realized by LiFTCM yield satisfactory results.

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.

scholarly journals Regional policy mixes for enterprise and innovation: A fuzzy-set clustering approach

2017 ◽  
Vol 36 (1) ◽  
pp. 28-46 ◽  
Author(s):  
Annalisa Caloffi ◽  
Marco Mariani
Keyword(s):  
Empirical Analysis ◽  
Fuzzy Set ◽  
Regional Policy ◽  
Clustering Techniques ◽  
Different Types ◽  
Clustering Approach ◽  
Policy Mixes ◽  
The Empirical Analysis ◽  
Regional Enterprise ◽  
Innovation Policies

The paper identifies different regional policy mixes, ranging from the more minimal to the more proactive or entrepreneurial and verifies their diffusion in the Italian regional enterprise and innovation policies. The empirical analysis is based on an original database containing every enterprise and innovation programme that has been implemented in Italy from 2007 to 2013, and is carried out by means of fuzzy-set clustering techniques. The results show the existence of remarkable heterogeneity, partly reflecting the well-known North-South divide, with some regions adopting minimal policy mixes and other regions adopting different types of proactive mixes.

An Empirical Analysis of the Obtrusiveness of and Participants' Compliance with the Electronically Activated Recorder (EAR)

2007 ◽  
Vol 23 (4) ◽  
pp. 248-257 ◽  
Author(s):  
Matthias R. Mehl ◽  
Shannon E. Holleran
Keyword(s):  
Empirical Analysis ◽  
Sampling Method ◽  
Daily Life ◽  
Assessment Method ◽  
Data Confidentiality ◽  
Short Term ◽  
Naturally Occurring ◽  
Two Samples ◽  
Audio Recorder ◽  
Term Monitoring

Abstract. In this article, the authors provide an empirical analysis of the obtrusiveness of and participants' compliance with a relatively new psychological ambulatory assessment method, called the electronically activated recorder or EAR. The EAR is a modified portable audio-recorder that periodically records snippets of ambient sounds from participants' daily environments. In tracking moment-to-moment ambient sounds, the EAR yields an acoustic log of a person's day as it unfolds. As a naturalistic observation sampling method, it provides an observer's account of daily life and is optimized for the assessment of audible aspects of participants' naturally-occurring social behaviors and interactions. Measures of self-reported and behaviorally-assessed EAR obtrusiveness and compliance were analyzed in two samples. After an initial 2-h period of relative obtrusiveness, participants habituated to wearing the EAR and perceived it as fairly unobtrusive both in a short-term (2 days, N = 96) and a longer-term (10-11 days, N = 11) monitoring. Compliance with the method was high both during the short-term and longer-term monitoring. Somewhat reduced compliance was identified over the weekend; this effect appears to be specific to student populations. Important privacy and data confidentiality considerations around the EAR method are discussed.

Sign in / Sign up

Export Citation Format

Share Document