A Bag Theoretic Approach towards the Count of an Intuitionistic Fuzzy Set

This study introduces a revised definition for fuzzy bags. It is based on the definition of bags given by Delgado et al. (Int J Intell Syst 2009;24:706–721) in which each bag has two parts: function and summary information. The new definition is given as a special case of L‐fuzzy bags, where L is a complete lattice. By some examples, the new concept is illustrated. Furthermore, the algebraic structure of L‐fuzzy bags is studied and the concept of α‐cuts, L‐fuzzy bag relations, and related theorems are given.

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“…2 So far, several works have been done using this new concept; see Refs. [3][4][5][6][7][8][9]. Also, bags have been employed in practice.…”

Section: Introductionmentioning

confidence: 99%

On Algebraic Structure of L-Fuzzy Bags

Kouchakinejad

Mashinchi

Mesiar

2016

Int. J. Intell. Syst.

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MMeMeR: An Algorithm for Clustering Heterogeneous Data using Rough Set Theory

Tripathy¹,

Goyal²,

Chowdhury³

et al. 2017

IJISA

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Abstract-In recent times enumerable number of clustering algorithms have been developed whose main function is to make sets of objects having almost the same features. But due to the presence of categorical data values, these algorithms face a challenge in their implementation. Also some algorithms which are able to take care of categorical data are not able to process uncertainty in the values and so have stability issues. Thus handling categorical data along with uncertainty has been made necessary owing to such difficulties. So, in 2007 MMR algorithm was developed which was based on basic rough set theory. MMeR was proposed in 2009 which surpassed the results of MMR in taking care of categorical data and it could also handle heterogeneous values as well. SDR and SSDR were postulated in 2011 which were able to handle hybrid data. These two showed more accuracy when compared to MMR and MMeR. In this paper, we further make improvements and conceptualize an algorithm, which we call MMeMeR or Min-Mean-Mean-Roughness. It takes care of uncertainty and also handles heterogeneous data. Standard data sets have been used to gauge its effectiveness over the other methods.

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A Bag Theoretic Approach towards the Count of an Intuitionistic Fuzzy Set

Cited by 2 publications

References 12 publications

On Algebraic Structure of L-Fuzzy Bags

On Algebraic Structure of L-Fuzzy Bags

MMeMeR: An Algorithm for Clustering Heterogeneous Data using Rough Set Theory

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