A new algorithm for initial cluster centers in k-means algorithm

Erişoğlu, Murat; Çaliş, Nazif; Sakallioglu, Saduiiah

doi:10.1016/j.patrec.2011.07.011

Cited by 168 publications

(85 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The maximum and the average FM indices produced by our method are higher than these of the other three algorithms when the number of the clusters reaches 10 and then 20. And it is noticed that the increase of the speed of our method is lower than the increase of the speed of Murat Erisoglu's method [4] for this group of data sets. Murat Erisoglu's method has best result when the number of the clusters is 5.…”

Section: B Experiments On Several Data Setsmentioning

confidence: 68%

“…The traditional kmeans method chooses initial cluster centers arbitrarily, which may affect its accuracy in the clustering [ [3], [4]]. …”

Section: Introductionmentioning

confidence: 99%

“…[ [4]]. This method first chooses two main dimensions that best represent the distribution of the dataset, and then computes Euclidean distances between each data point and the centroid of the data in the subspace defined by the two selected dimensions.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A new algorithm for choosing initial cluster centers for k-means

Qiao¹,

Lu²

2013

Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)

View full text Add to dashboard Cite

Abstract-The k-means algorithm is widely used in many applications due to its simplicity and fast speed. However, its result is very sensitive to the initialization step: choosing initial cluster centers. Different initialization algorithms may lead to different clustering results and may also affect the convergence of the method. In this paper, we propose a new algorithm for improving the initialization of the cluster centers by reducing dimensions followed by moving cluster centers towards high density regions. Our algorithm is compared with three other initialization algorithms for k-means. And the effectiveness of our approach is shown by a series of carefully designed experiments.

show abstract

Section: B Experiments On Several Data Setsmentioning

confidence: 68%

“…The traditional kmeans method chooses initial cluster centers arbitrarily, which may affect its accuracy in the clustering [ [3], [4]]. …”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A new algorithm for choosing initial cluster centers for k-means

Qiao¹,

Lu²

2013

Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)

View full text Add to dashboard Cite

show abstract

“…Unfortunately, using standard KMeans algorithm, certain objects that represent same rank may not be grouped together because each objects is measured based on closest distance to the centroids rather than among themselves. As clustering result is influenced by initial centroids selection [11], they need also to be targeted towards relevant points of rankingbased cluster representation. Many suggestions have been made on initial centroids enhancement.…”

Section: Related Workmentioning

confidence: 99%

A hybrid model of ordinal ranking-based clustering using G+Rank K-Means

Suhailan¹,

Samad²,

Burhanuddin³

et al. 2018

IJET

View full text Add to dashboard Cite

K-Means is a clustering technique that maps object features onto multidimensional coordinates and groups them based on location closeness. However, measuring closest distance can be doubtful when ranking representation of ordinal scale objects are not considered. Thus, distribution of objects in a cluster may violate ranking representation. For example, a same-rank object may be grouped into different clusters. To address this issue, an enhanced of K-Means algorithm is proposed to achieve better and meaningful result of ranking-based clustering. It is based on integration of ranking algorithm that sort objects into ranking list which also representing object closeness based on its nearby location. A new additional step in K-Means is proposed in reassigning unaligned K-Means nearest objects using ranking attribute that eventually accelerates the clustering process. AHP ranking algorithm is integrated into K-Means in achieving a ranking-based cluster. This enhancement was evaluated on three ordinal datasets covering 67 Java programs, 92 students' marks on computer architecture subject and 456 EUFA's football club coefficient ranking list. The results show that by integrating ranking algorithm in K-Means as proposed in G + Rank K-Means, a rank cluster representation has been successfully achieved. The purity value that represents the correctness against certain group classification has also increased.

show abstract

“…Each clustering algorithm has been applied with 9 different parameterizations. We consider 3 kernels (cosine distance, Euclidean distance and Mahalanobis distance) and 3 cluster center initialization methods (random, cumulative approach 26 and subtractive clustering 27 ).…”

Section: Construction Of H-fuzzy Partitionsmentioning

confidence: 99%

A Definition for Hesitant fuzzy Partitions

Aliahmadipour¹,

Torra²,

Eslami³

et al. 2016

IJCIS

View full text Add to dashboard Cite

In this paper, we define hesitant fuzzy partitions (H-fuzzy partitions) to consider the results of standard fuzzy clustering family (e.g. fuzzy c-means and intuitionistic fuzzy c-means). We define a method to construct H-fuzzy partitions from a set of fuzzy clusters obtained from several executions of fuzzy clustering algorithms with various initialization of their parameters. Our purpose is to consider some local optimal solutions to find a global optimal solution also letting the user to consider various reliable membership values and cluster centers to evaluate her/his problem using different cluster validity indices.

show abstract

A new algorithm for initial cluster centers in k-means algorithm

Cited by 168 publications

References 5 publications

A new algorithm for choosing initial cluster centers for k-means

A new algorithm for choosing initial cluster centers for k-means

A hybrid model of ordinal ranking-based clustering using G+Rank K-Means

A Definition for Hesitant fuzzy Partitions

Contact Info

Product

Resources

About