Applying Rough Set Concepts to Clustering

Lingras, Pawan; Peters, Georg

doi:10.1007/978-1-4471-2760-4_2

Cited by 52 publications

(30 citation statements)

References 14 publications

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“…• Finally, if each m i is logical, with m i (A i ) = 1 for some A i ⊆ Ω, we can define lower and upper approximations of each cluster, as in rough clustering [20,27]. The lower approximation of cluster ω k is the set of objects that surely belong to ω k ,…”

Section: Credal Partitionmentioning

confidence: 99%

“…Over the years, the notion of partitional clustering has been extended to several important variants, including fuzzy [2] and possibilistic [16] clustering, and more recently, rough [20,27] and evidential [7,25] clustering. Contrary to classical (hard) partitional clustering, in which each object is assigned unambiguously and with full certainty to a single cluster, these variants allow for ambiguity, uncertainty or doubt in the assignment of objects to clusters.…”

Section: Introductionmentioning

confidence: 99%

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Evidential clustering of large dissimilarity data

Denźux

Sriboonchitta

Kanjanatarakul

2016

Knowledge-Based Systems

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In evidential clustering, the membership of objects to clusters is considered to be uncertain and is represented by Dempster-Shafer mass functions, forming a credal partition. The EVCLUS algorithm constructs a credal partition in such a way that larger dissimilarities between objects correspond to higher degrees of conflict between the associated mass functions. In this paper, we present several improvements to EVCLUS, making it applicable to very large dissimilarity data. First, the gradient-based optimization procedure in the original EVCLUS algorithm is replaced by a much faster iterative row-wise quadratic programming method. Secondly, we show that EVCLUS can be provided with only a random sample of the dissimilarities, reducing the time and space complexity from quadratic to roughly linear. Finally, we introduce a two-step approach to construct credal partitions assigning masses to selected pairs of clusters, making the algorithm outputs more informative than those of the original EVCLUS, while remaining manageable for large numbers of clusters.

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Section: Credal Partitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evidential clustering of large dissimilarity data

Denźux

Sriboonchitta

Kanjanatarakul

2016

Knowledge-Based Systems

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“…Ahmed et al [4] has imagined Bias Corrected Fuzzy c-Means [BCFCM] for managing predisposition field in mind pictures. Chuang et al [20] proposed fuzzy c-implies grouping with spatial data to manage power non-consistency and to expel uproarious spots amid picture division. Yang et al [21], LiMaand Staunton [22], Jiayin Kang et al [23], and Zhou Xiancheng et al [24] have proposed novel altered fuzzy c-implies calculation by joining the spatial neighborhood data into the standard FCM calculation to evacuate force inhomogeneities in therapeutic pictures.…”

Section: Review Of Related Workmentioning

confidence: 99%

Enhanced and Explored Intuitionistic Rough Based Fuzzy C-means Approach for MR Brain Image Segmentation

Prasanthi¹,

Nagamalleswararao²

2018

IJET

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Segmentation of magnetic resonance images is medically complex and important for study and diagnosis of medical brain images, because of its sensitivity in terms of noise for brain medical images. These are the main issues in classification of brain images. Because of uncertainty & vagueness of brain medical images, so that rough sets, fuzzy sets and Rough sets are mathematical tools evaluate and handle uncertainty and vagueness in medical brain images. Traditionally, different type of fuzzy sets, Rough sets and rough sets based approaches were introduced, they have different several drawbacks with respect to different parameters. So this paper introduces a novel image segmentation calculation method i.e. Enhanced and Explored Intuitionistic Rough based Fuzzy C-means Approach (EEISFCMA) with estimation of weight bias parameter for brain image segmentation. Intuitionistic Rough based fuzzy sets are generalized form of fuzzy, Rough sets and their representative elements are evaluated with non-membership and membership value. Proposed algorithm of this paper consists standard features of existing clustering without spatial weight context data, it defines sensitive of noise in brain images, so that our proposed algorithm deals with intensity and noise reduction of brain image effectively. Furthermore, to reduce iterations in clustering, proposed algorithm initializes cluster centroid based on weight measure using max-dist evaluation method before execution of proposed algorithm. Experimental results of proposed approach carried out efficient image segmentation results compared to existing segmented approaches developed in brain image and other related images. Mainly proposed approach have consists better experimental evaluation based on results.

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“…Rough Sets has also been applied in the field of clustering. The work in [15], for instance, reviews some of methods that make use of Rough Sets for clustering in the areas such as traffic data (clustering highway sections), and clustering supermarket customers. Using Rough Sets to predict the failure of Ch inese companies has been explored in [16] where the authors employ the variable precision Rough Sets model to find the impact of financial and nonfinancial parameters on companies' performance.…”

Section: Applications Of the Theory Of Rough Setsmentioning

confidence: 99%

A Rough Sets-based Agent Trust Management Framework

Abedinzadeh

Sadaoui²

2013

IJISA

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Abstract-In a virtual society, which consists of several autonomous agents, trust helps agents to deal with the openness of the system by identify ing the best agents capable of performing a specific task, or achieving a special goal. In this paper, we introduce ROSTAM, a new approach for agent trust management based on the theory of Rough Sets. ROSTAM is a generic trust management framework that can be applied to any types of mu lti agent systems. However, the features of the application do main must be provided to ROSTAM. These features form the trust attributes. By collecting the values for these attributes, ROSTAM is able to generate a set of trust rules by employing the theory of Rough Sets. ROSTAM then uses the trust rules to extract the set of the most trusted agents and forwards the user's request to those agents only. After getting the results, the user must rate the interaction with each trusted agent. The rating values are subsequently utilized for updating the trust rules. We applied ROSTAM to the domain o f cross -language Web search. The resulting Web search system reco mmends to the user the set of the most trusted pairs of translator and search engine in terms of the pairs that return the results with the highest precision of retrieval.

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Applying Rough Set Concepts to Clustering

Cited by 52 publications

References 14 publications

Evidential clustering of large dissimilarity data

Evidential clustering of large dissimilarity data

Enhanced and Explored Intuitionistic Rough Based Fuzzy C-means Approach for MR Brain Image Segmentation

A Rough Sets-based Agent Trust Management Framework

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