Introduction to Fuzzy Clustering

Sato‐Ilic, Mika; Jain, Lakhmi C.

doi:10.1007/3-540-34357-1_1

Cited by 14 publications

(11 citation statements)

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“…So, the possibilistic approach to clustering is more general and flexible approach to clustering than the fuzzy approach. Many fuzzy and possibilistic clustering algorithms could be found in the corresponding books [3], [4], [5], [6].…”

Section: Preliminary Remarksmentioning

confidence: 99%

“…x -price, 4 x -comfortable degree, 5 x -design, 6 x -safety coefficient, as in the first example. The first object is the typical point of the first intuitionistic fuzzy cluster, the second object is the typical point of the second intuitionistic fuzzy cluster, the fifth object is the typical point of the third intuitionistic fuzzy cluster and the fourth object is the typical point of the fourth intuitionistic fuzzy cluster.…”

Section: The Second Examplementioning

confidence: 99%

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Intuitionistic Heuristic Prototype-based Algorithm of Possibilistic Clustering

Viattchenin¹,

Shyrai²

2015

CAE

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This paper introduces a novel intuitionistic fuzzy set-based heuristic algorithm of possibilistic clustering. For the purpose, some remarks on the fuzzy approach to clustering are discussed and a brief review of intuitionistic fuzzy set-based clustering procedures is given, basic concepts of the intuitionistic fuzzy set theory and the intuitionistic fuzzy generalization of the heuristic approach to possibilistic clustering are considered, a general plan of the proposed clustering procedure is described in detail, two illustrative examples confirm good performance of the proposed algorithm, and some preliminary conclusions are formulated.

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Section: Preliminary Remarksmentioning

confidence: 99%

Section: The Second Examplementioning

confidence: 99%

Intuitionistic Heuristic Prototype-based Algorithm of Possibilistic Clustering

Viattchenin¹,

Shyrai²

2015

CAE

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“…For this pur-pose we should use the vector to represent each of the demand information [4]. Each element of the vector represent the values of certain characteristics of demand information.…”

Section: The Characteristic Indexes Of Project Stakeholders Demands Imentioning

confidence: 99%

The Dynamic Fuzzy Clustering of the Project Stakeholders Demands Information

Wang¹,

Houli²,

Zuhe³

2015

TOCSJ

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How to carry out the stakeholders managements effectively, one major issue is to continuously meet the demands of the stakeholders. The purpose of this paper is to clarify the disorganized information of stakeholders demands and correctly deal with it. The paper has expounded on the project stakeholders' demands information, such as fuzziness, randomness, dynamics, diversity and contradictoriness, through the analysis of fuzzy equivalence matrix to find the basic method and procedure of application of fuzzy equivalence relation classification by using the theory of fuzzy mathematics. Based on the fuzzy similarity matrix by using the least squares method derives its transitive closure, and solves the fuzzy clustering problem of fuzzy similarity relation. The paper verifies the effectiveness and feasibility about the dynamic fuzzy clustering of the stakeholders demands information by example, based on the principle of fuzzy clustering, studied the establishment and application of stakeholders' demands information related to dynamic fuzzy clustering models, set the characteristic indexes of project stakeholders demands information classification. Established the relevant theory of fuzzy clustering based on stakeholders demands information fuzzy clustering model, and study the specific implementation steps on how to use the model. These results have practical significance for the classification of managing complex stakeholders' demands information.

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“…Sato-Ilic and Jain [18]). An interpretation of the obtained principal components can be made on the basis of factor loading.…”

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confidence: 96%

“…An interpretation of the obtained principal components can be made on the basis of factor loading. The factor loading is defined as a correlation coefficient between the v -th principal component v z and the t-th attribute ˆt x , 1, , = t m K , as follows [18]: Table 4 the values of factor loadings (19) are shown which can represent a relationship between each principal component and each attribute.…”

mentioning

confidence: 99%