Modern Algorithms of Cluster Analysis

Wierzchoń, Sławomir T.; Kłopotek, Mieczysław A.

doi:10.1007/978-3-319-69308-8

Cited by 131 publications

(124 citation statements)

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“…Next, the inputted data were standardized using the formula (x il − μ l )/σ l , where μ l , σ l are the average value and the standard deviation of the l-th feature (see e.g. Wierzchoń & Kłopotek, 2018).…”

Section: Methodsmentioning

confidence: 99%

National fiscal frameworks in the post-crisis European Union

Szymańska

2018

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Research background: The latest economic and financial crisis has seriously injured European Union Member States, affecting the condition of their public finances. In the face of the crisis, the EU made a special effort to increase the effectiveness of national fiscal frameworks, e.g. by improving the compliance with legislation. The post 2009 reforms were aimed at providing a solid economic foundation for the national fiscal frameworks, especially in the high-debt euro area countries. Purpose of the article: The goal of this research is twofold. Firstly, it aims to provide an outline of the national fiscal governance in the EU. Secondly, the paper analyzes the changes in the core measures of fiscal governance in the EU between the crisis period and the year 2016 (due to the latest available data) and investigates the similarities in the progress made by the 28 EU countries in restoring balance in public finance. Methods: To achieve the goal, the literature review and the analysis of core elements of national fiscal frameworks are provided. In the empirical section the grouping method for all 28 EU countries based on the Ward's agglomerative hierarchical clustering method is employed. The study uses data derived from the AMECO database (in the case of fiscal data) and the European Commission thematic data for quality indexes of particular elements of fiscal governance (numerical fiscal rules, medium-term budgetary frameworks and independent fiscal institutions). Findings & Value added: This paper contributes to the literature by, on the one hand, attempting to analyze changes in main fiscal governance measures and, on the other hand, by assessing their link with public finance through employment of the agglomerative clustering method. Based on the results, the conclusion about the importance of the improvement in fiscal frameworks is provided. The analysis shows that countries with better national fiscal framework achieved better results in public finances regardless the macroeconomic conditions.

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Section: Methodsmentioning

confidence: 99%

National fiscal frameworks in the post-crisis European Union

Szymańska

2018

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“…Step 6: the S ij values are clustered by the fuzzy clustering method [15], and these wind turbines can be clustered into k groups {g 1 , g 2 , . .…”

Section: A Svd Clustering Algorithm Of Offshore Wind Farmmentioning

confidence: 99%

“…e k-means clustering algorithm divides wind turbines into several groups [14]. However, the wind farm wake model is a high-dimensional mathematical model, and the k-means clustering and the support vector clustering algorithms are inefficient and easily converted to a local minimum with more dimensions; at the same time, the results of two clustering algorithms are poor robustness [15]. To solve the high-dimensional problem of wind turbine clustering, SVD (singular value decomposition) is an effective clustering algorithm for large datasets [15].…”

Section: Introductionmentioning

confidence: 99%

Wind Turbine Clustering Algorithm of Large Offshore Wind Farms considering Wake Effects

Zhang

2019

Mathematical Problems in Engineering

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This paper proposed the SVD (singular value decomposition) clustering algorithm to cluster wind turbines into some group for a large offshore wind farm, in order to reduce the high-dimensional problem in wind farm power control and numerical simulation. Firstly, wind farm wake relationship matrixes are established considering the wake effect in an offshore wind farm, and the SVD of wake relationship matrixes is used to cluster wind turbines into some groups by the fuzzy clustering algorithm. At last, the Horns Rev offshore wind farm is analyzed to test the clustering algorithm, and the clustering result and the power simulation show the effectiveness and feasibility of the proposed clustering strategy.

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“…Data mining is the process for sorting the information in large database to predict trend and behavior. This method analyzes existing data to discover patterns of relationship and to gain new information [24]. A wide variation in terms of professional fields, formulations, and data management techniques as well as the possibility to choose important data become necessary to consider for the application of data mining technique [25].…”

Section: Data Mining Techniquesmentioning

confidence: 99%

“…As regards of data clustering, two important questions can be posed about (a) the similarity of the objects in each cluster, and (b) the method to identify such similarity. Wierzchoń used cluster analysis of multi-predictors technique to separate and cluster the customers of a bank in credit granting [24]. His study points out that the clustering is accurate and practical.…”

Section: Data Mining Techniquesmentioning

confidence: 99%

Application of Kansei Engineering and Data Mining in Developing an Ingenious Product Co-design System

Sakornsathien¹,

Sinthupinyo²,

Anuntavoranich³

2019

IJMLC

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This research intends to be a part in developing an automatic system that shows the potential of designing a product form by co-designing with the user in order to make it suitable for each consumer by applying Kansei Engineering Technique. However, the operation of KE needs data collection from specific target group in order to devise a formula and interpret the results when applying to study the same target group, or else the accuracy will be reduced. In order to eliminate the drawbacks, Data Mining techniques will be applied with KE system in order to use as an ingenious product co-design system.The style and preference of each user will be used as a categorizing factor clustering the database into groups with K-means technique, in this research. Each classifying cluster will use its own database in the system processing in order to gain a set of design elements precisely from the system. Decision Tree classification technique is selected for the study. For the model validation, we apply the cross-validation as an unbiased model performance evaluation. In order to build the KE system, the sentiments in this research are indicated by 5 pair-words constituting the system data. The result shows that, this method offers more accurate prediction of design elements comparing to the method which undivided users.Index Terms-Data mining, machine learning, Kansei engineering, product development.

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Modern Algorithms of Cluster Analysis

Cited by 131 publications

References 0 publications

National fiscal frameworks in the post-crisis European Union

National fiscal frameworks in the post-crisis European Union

Wind Turbine Clustering Algorithm of Large Offshore Wind Farms considering Wake Effects

Application of Kansei Engineering and Data Mining in Developing an Ingenious Product Co-design System

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