Clustering of Categorical Data Using Intuitionistic Fuzzy k-modes

2017 ◽  
pp. 254-263 ◽  
Author(s):  
Darshan Mehta ◽  
B. K. Tripathy
Keyword(s):  
Categorical Data ◽  
Intuitionistic Fuzzy

An Intuitionistic Fuzzy Approach With Rough Entropy Measure to Detect Outliers in Two Universal Sets

2020 ◽  
Vol 9 (3) ◽  
pp. 100-117
Author(s):  
Sangeetha T. ◽  
Geetha Mary A.
Keyword(s):  
Outlier Detection ◽  
Categorical Data ◽  
Numerical Data ◽  
Entropy Measure ◽  
Weighted Density ◽  
Intuitionistic Fuzzy ◽  
Decision Attributes ◽  
Density Values ◽  
Class Labels ◽  
Massive Databases

The process of recognizing patterns, collecting knowledge from massive databases is called data mining. An object which does not obey and deviates from other objects by their characteristics or behavior are known as outliers. Research works carried out so far on outlier detection were focused only on numerical data, categorical data, and in single universal sets. The main goal of this article is to detect outliers significant in two universal sets by applying the intuitionistic fuzzy cut relationship based on membership and non-membership values. The proposed method, weighted density outlier detection, is based on rough entropy, and is employed to detect outliers. Since it is unsupervised, without considering class labels of decision attributes, weighted density values for all conditional attributes and objects are calculated to detect outliers. For experimental analysis, the Iris dataset from the UCI repository is taken to detect outliers, and comparisons have been made with existing algorithms to prove its efficiency.

scholarly journals Application of Genetic Algorithm Based Intuitionistic Fuzzy k-Mode for Clustering Categorical Data

2017 ◽  
Vol 17 (4) ◽  
pp. 99-113
Author(s):  
Akarsh Goyal ◽  
Patra Anupam Sourav ◽  
P. Kalyanaraman
Keyword(s):  
Data Mining ◽  
Genetic Algorithm ◽  
Categorical Data ◽  
Clustering Algorithms ◽  
Large Datasets ◽  
Objective Functions ◽  
Intuitionistic Fuzzy ◽  
Mode Method ◽  
Consistent Performance ◽  
Data Objects

AbstractIn present times a great number of clustering algorithms are available which group objects having similar features. But most of the datasets have data values that are categorical, which makes it difficult to implement these algorithms. The concept of genetic algorithm on intuitionistic fuzzy k-Mode method is proposed in the paper to cluster categorical data. This model is an extension of intuitionistic fuzzy k-Mode in which the notion of fitness related objective functions, crossovers, mutations and probability has been added to provide better clusters for the data objects. Also the intuitionistic parameter has been retained for the calculation of membership values of element x in a given cluster. UCI repository datasets were used for assessing efficacy of algorithms. The qualified analysis and results depict much consistent performance, where a significant improvement is achieved as compared to intuitionistic fuzzy k-Mode and simulated annealing based intuitionistic fuzzy k-mode. Genetic Algorithm based intuitionistic fuzzy k-Mode is very efficient when clustering is applied on large datasets that are categorical in nature, which proves to be very critical for data mining processes.

Analyzing sequential categorical data on dyadic interaction: A comment on Gottman.

1982 ◽  
Vol 91 (2) ◽  
pp. 393-403 ◽  
Author(s):  
Paul D. Allison ◽  
Jeffrey K. Liker
Keyword(s):  
Categorical Data ◽  
Dyadic Interaction

Decision making based on intuitionistic fuzzy preference relations with additive approximate consistency

2020 ◽  
Vol 39 (3) ◽  
pp. 4041-4058
Author(s):  
Fang Liu ◽  
Xu Tan ◽  
Hui Yang ◽  
Hui Zhao
Keyword(s):  
Decision Making ◽  
Decision Makers ◽  
Real Interval ◽  
Preference Relations ◽  
Intuitionistic Fuzzy ◽  
Proposed Model ◽  
Fuzzy Preference Relations ◽  
Novel Concept ◽  
Fuzzy Weights ◽  
Fuzzy Preference

Intuitionistic fuzzy preference relations (IFPRs) have the natural ability to reflect the positive, the negative and the non-determinative judgements of decision makers. A decision making model is proposed by considering the inherent property of IFPRs in this study, where the main novelty comes with the introduction of the concept of additive approximate consistency. First, the consistency definitions of IFPRs are reviewed and the underlying ideas are analyzed. Second, by considering the allocation of the non-determinacy degree of decision makers’ opinions, the novel concept of approximate consistency for IFPRs is proposed. Then the additive approximate consistency of IFPRs is defined and the properties are studied. Third, the priorities of alternatives are derived from IFPRs with additive approximate consistency by considering the effects of the permutations of alternatives and the allocation of the non-determinacy degree. The rankings of alternatives based on real, interval and intuitionistic fuzzy weights are investigated, respectively. Finally, some comparisons are reported by carrying out numerical examples to show the novelty and advantage of the proposed model. It is found that the proposed model can offer various decision schemes due to the allocation of the non-determinacy degree of IFPRs.

INTUITIONISTIC FUZZY INTERPRETATION OF INFORMATION MESSAGES

1999 ◽  
Vol 04 (01) ◽  
Author(s):  
K. Atanassov ◽  
D. Dimitrov
Keyword(s):  
Intuitionistic Fuzzy
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