Evaluation of k-Medoids and Fuzzy C-Means clustering algorithms for clustering telecommunication data

Velmurugan, T.

doi:10.1109/incoset.2012.6513891

Cited by 15 publications

(12 citation statements)

References 5 publications

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“…Many methods are used for data organization in clusters according to their similarities. The clustering process is usually based on measuring similarities of attributes in data [14].…”

Section: K-means Clusteringmentioning

confidence: 99%

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Evaluation of Croatian Development Strategies Using SWOT Analyses with Fuzzy TOPSIS Method and K-Means Methods

Trstenjak¹,

Kavran²,

Bujan³

2015

JOEBM

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Abstract-The purpose of this paper is to analyse Croatian national development strategy and Croatian local/regional development strategies. Upon defining the purpose we can define the main goal of this paper and which is creation of a tool based on funny logics which will give results on whether the chosen areas of analysed strategies are correspondent or not. For that purposes linguistic variables for areas of importance of national and regional development strategies have been defined with corresponding weight impact factors. Defined areas have been of various development priorities and have been sorted according to SWOT analysis components. Upon definition of variables and weight impact factors the analysis has been conducted.Index Terms-Development strategy, fuzzy TOPSIS, national strategy, k-means clustering. I. INTRODUCTIONTo start the story about the national and regional development strategies it is necessary to define personal development. We can say that development enriches the lives of individuals, reduces poverty, and improves well-fare, raises the quality of life in general. As it affects positively the individuals it also positively affects nations and regions. Given this definition of development, it is clear that a development strategy must be focused on transformation of society in identifying the barriers to, as well as potential catalysts for change 1 . When discussing a national level it is clear that the process of creation national and regional development strategy should be planned in details before adoption. National strategies, as well as local/regional strategies should include the component of sustainable development in every sector of an economy. Yet sustainable development touches many sensitive questions and values, therefore progress is somewhat slow 2.When states and local/regional authorities create development strategies they try to engage as many stakeholders as possible and during the process they create SWOT analysis. SWOT analysis is a marketing tool used in marketing planning but also in many other forms of planning 3. For the purposes of national SWOT analysis local stakeholder are engaged in the process of strategy Manuscript received November 5, 2013; revised January 17, 2014. B. Trstenjak is with the Dept. of Computer Engineering, Medjimurje University of Applied Sciences, Cakovec, Croatia (e-mail: btrstenjak@ mev.hr).A. K. Kavran and I. Bujan are with the Dept. of Management, Medjimurje University of Applied Sciences, Cakovec, Croatia (e-mail: akos@mev.hr, ibujan@mev.hr).development to contribute with their current issues, expectations and provide possible solutions. However, in some cases there is no correspondence between local/regional and national level. From the aforementioned arises the main goal of this paper, which is to create a tool with the help of fuzzy logics to discover to what extent are areas of Croatian national development strategy correspondent to the areas of local/regional development strategies in Croatia.The beginnings of fuzzy logics star...

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“…Many methods are used for data organization in clusters according to their similarities. The clustering process is usually based on measuring similarities of attributes in data [14].…”

Section: K-means Clusteringmentioning

confidence: 99%

“…Many methods are used for data organization in clusters according to their similarities. The clustering process is usually based on measuring similarities of attributes in data [14].K-Means algorithm is one of the popular unsupervised learning algorithms, relatively easy for implementation. The basic idea is to define k centroids, one for each cluster.…”

mentioning

confidence: 99%

Evaluation of Croatian Development Strategies Using SWOT Analyses with Fuzzy TOPSIS Method and K-Means Methods

Trstenjak¹,

Kavran²,

Bujan³

2015

JOEBM

View full text Add to dashboard Cite

show abstract

“…The main aim of K-mean Clustering is to minimize an objective functions. The objective function is defined as in a following way [20],…”

Section: K-mean Clusteringmentioning

confidence: 99%

“…Where x i (j) -c j 2 is a chosen distance measure between a data point x i (j) and the cluster center c j is an indicator of the distance of the n data points from their respective cluster centers [20]. The objective function is also called as cost function 1 .…”

Section: K-mean Clusteringmentioning

confidence: 99%

Real Time Iris Image Segmentation for Non Co-Operative Environment

Mayur¹,

Adhiya²

2014

IJCA

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“…In another study entitled Performance Evaluation of K-Means and Fuzzy C-Means Clustering Algorithms for Statistical Distributions of Input Data Points [12] explained that the two algorithms are implemented and its performance was analyzed based on the quality of the clustering. Behavior of both algorithms depends on the number of data points and the number of clusters.…”

Section: Literature Reviewmentioning

confidence: 99%

Identification of Potential Student Academic Ability using Comparison Algorithm K-Means and Farthest First

Dewi¹,

Utomo²,

Yulianto³

2013

IJCA

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Evaluation of k-Medoids and Fuzzy C-Means clustering algorithms for clustering telecommunication data

Cited by 15 publications

References 5 publications

Evaluation of Croatian Development Strategies Using SWOT Analyses with Fuzzy TOPSIS Method and K-Means Methods

Evaluation of Croatian Development Strategies Using SWOT Analyses with Fuzzy TOPSIS Method and K-Means Methods

Real Time Iris Image Segmentation for Non Co-Operative Environment

Identification of Potential Student Academic Ability using Comparison Algorithm K-Means and Farthest First

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