An ordered clustering algorithm based on K-means and the PROMETHEE method

Chen, Liuhao; Xu, Zeshui; Wang, Hai; Liu, Shousheng

doi:10.1007/s13042-016-0617-9

Cited by 40 publications

(27 citation statements)

References 28 publications

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“…Generally, the K-means algorithm uses Euclidean Distance Formula (EDF) to calculate the distance among the data. Suppose there is a set of points in m-dimensional Euclidean space [11]: S={X 1 , X 2 ,..., X n }, and X i =(x i1 , x i2 ,..., x im ) (i=1, 2,..., n).…”

Section: Related Workmentioning

confidence: 99%

“…In this space, select k center points within a certain range V i (i=1, 2,..., k) [11]. Guarantee that the sum of the squares of the distances which are the n points to their own centers is the smallest [11].…”

Section: Related Workmentioning

confidence: 99%

“…Guarantee that the sum of the squares of the distances which are the n points to their own centers is the smallest [11]. Use algebraic formulas to express:…”

Section: Related Workmentioning

confidence: 99%

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Clustering Analysis on the Introduction of Talents in Colleges

Fang¹

2018

IJDST

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Abstract:With the development of economy and technology, introducing and training talents have become the key driving force in the world which can enhance the competitive strength of the whole countries. Therefore, the strategies of strengthening the universities and colleges with more talented people and making efforts to implement the construction of "Double top" are put forward in the same time. Methods of clustering analysis have been widely used in the actual researches. In this study, an effective clustering analysis model by comparing the clustering analysis under different dimensionality reduction methods is established. Firstly, preprocess the data about talent introduction which is collected from Zhejiang University of Finance and Economics, and use Principal Component Analysis (PCA), Weighted Principal Component Analysis (Weighted-PCA) and Random Forest (RF) to reduce the dimensions of the data. Next, use K-means clustering algorithm and K-medoids clustering algorithm to cluster the preprocessed data. The classification results indicate that the K-medoids algorithm with Weighted-PCA is superior to other clustering methods in this illustrative case. In addition, the experiment divides talents into high-end talents and mid-end talents. By looking into the analysis of the characteristics of the clustering results, some targeted advices on the talents introduction in colleges can be provided.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Clustering Analysis on the Introduction of Talents in Colleges

Fang¹

2018

IJDST

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“…It is again possible, in agreement with general opinion, to speak about the complexity of the issues to be resolved, both from the experimental and analytical perspective. From numerous published research works, which dealt with these issues, and which can be used as sources of opinions, we can cite the works [5][6][7][8][9][10][11][12][13][14][15][16][17][18]. The issues concerning the laser and other technologies using flexible working tool are complicated by considerable differences of the tool feed rate along the surface of the sample in comparison with the rate of penetration of the disintegration tool into material, i.e.…”

Section: Process Timingmentioning

confidence: 99%

Control of laser cutting by process timing

Harničárová

Valíček

Kušnerová

et al. 2015

Materialwissenschaft Werkst

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Investigation into the assessment of the end product quality of laser cutting becomes essential. There is still a substantial number of research questions that are dedicated to such things as the analysis of cutting problems, or control of laser cutting. This study proposes a procedure for timing of the process by solution of the implicitly expressed relation between the function of time and the depth of cut. The solution realised in the research paper provides the following: a derivation of the method of identification of the influence of the time parameter and of timing of the process functions in the cut.

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“…Major shortcomings of the present-day state are that test methods are not yet developed, which could be used in unambiguous testing of the basic properties of the cut material, i.e. the cuttability of the material using an abrasive waterjet, in advance, further a waterjet, a method not yet developed, which could be used in practice and research for the determination of the optimum technology parameters (traverse speed of the cutting head, pump pressure, amount, size and kind of abrasive material, and others) according to the pre-determined cuttability of a specific material using an abrasive waterjet (AWJ) [4][5][6][7][8][9][10][11][12][13][14][15][16]. On the basis of the above-presented causes, a uniform technological classification of engineering materials according to deformation parameters is still missing, because of the lack of a suitable generalizing criterion which would unambiguously characterize the cut material [17][18][19][20][21][22][23][24][25][26].…”

mentioning

confidence: 99%

A new approach for the determination of technological parameters for hydroabrasive cutting of materials

Valíček

Harničárová

Hlavatý

et al. 2016

Materialwissenschaft Werkst

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A method for the design of a technology for the abrasive waterjet cutting of materials is based on the determination of the constant of cuttability of materials using an abrasive waterjet Kawj according to four different versions. Version A, where three deformation parameters are measured on the test cut/sample, namely selected depth hx [mm], local roughness Rax [μm] at depth hx and local deviation of the cut trace from the normal plane Yretx at depth hx. Version B, where merely two deformation parameters are measured, namely the reference depth het [mm] and deviation of the cut trace from the normal plane Yretx [mm] or, according to version C, where one parameter is mearured, namely either the ultrasonic wave speed vLUZ [m · s–1] or Young's modulus Emat [MPa]; Kawj being calculated from relevant deformation parameters. The last option is version D, where the design of the whole technology is carried out by calculation of Kawj according to the Young's modulus Emat or according to the ultrasonic wave speed vLUZ of the cut material.

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An ordered clustering algorithm based on K-means and the PROMETHEE method

Cited by 40 publications

References 28 publications

Clustering Analysis on the Introduction of Talents in Colleges

Clustering Analysis on the Introduction of Talents in Colleges

Control of laser cutting by process timing

A new approach for the determination of technological parameters for hydroabrasive cutting of materials

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