Hierarchical initialization approach for K-Means clustering

Lu, Jiewei; Tang, Jiaowei; Tang, Zhenmin; Yang, Jingyu

doi:10.1016/j.patrec.2007.12.009

Cited by 71 publications

(31 citation statements)

References 5 publications

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“…The centers are then given through the centers of these clusters. Lu et al's method applies a two-phase pyramidal method [32]. The attributes of each point are first coded as integers.…”

Section: IImentioning

confidence: 99%

Face Extraction from Image based on K-Means Clustering Algorithms

Farhang¹

2017

ijacsa

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Abstract-This paper proposed a new application of K-means clustering algorithm. Due to ease of implementation and application, K-means algorithm can be widely used. However, one of the disadvantages of clustering algorithms is that there is no balance between the clustering algorithm and its applications, and many researchers have paid less attention to clustering algorithm applications. The purpose of this paper is to apply the clustering algorithm application to face extraction. An improved K-means clustering algorithm was proposed in this study. A new method was also proposed for the use of clustering algorithms in image processing. To evaluate the proposed method, two case studies were used, including four standard images and five images selected from LFW standard database. These images were reviewed first by the K-means clustering algorithm and then by the RER-K-means and FE-RER-clustering algorithms. This study showed that the K-means clustering algorithm could extract faces from the image and the proposed algorithm used for this work increased the accuracy rate and, at the same time, reduced the number of iterations, intra cluster distance, and the related processing time.

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“…The centers are then given through the centers of these clusters. Lu et al's method applies a two-phase pyramidal method [32]. The attributes of each point are first coded as integers.…”

Section: IImentioning

confidence: 99%

Face Extraction from Image based on K-Means Clustering Algorithms

Farhang¹

2017

ijacsa

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“…To overcome the dependency of K-Means algorithm on the initial clusters centroids (instead of random generation) Likas et al, [15] have developed a global K-Means algorithm based on the deterministic global optimization and K-Means in which K-Means implemented as local search algorithms. To generate good initial cluster centers Lu et al, [16] have applied the hierarchical clustering approach with K-Means algorithm and this method required less iteration time and higher convergence speed but the method has some drawback such that the values of attributes must be numeric if the values are non-numeric then these values must be converted into numeric values. According to Meila & Heckerman [36] and De Amorim & Komisarczuk [31], there are large number of methods exist to refine and initialization of the clusters centers in K-Means but at present there is not a single method that can be recognized as universal method to generate initial cluster centers.…”

Section: Background and Related Workmentioning

confidence: 99%

A New Initialization Method to Originate Initial Cluster Centers for K-Means Algorithm

Kumar¹,

Sahoo²

2014

IJAST

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“…Lu et al [16] proposed another hierarchical initialization method to the k-means clustering problem. The core of this method is to treat the clustering problem as a weighted clustering problem so as to find better initial cluster centers based on the hierarchical approach.…”

Section: Related Workmentioning

confidence: 99%

Initializing KMeans Clustering Algorithm using Statistical Information

Eltibi¹,

Ashour²

2011

IJCA

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K-means clustering algorithm is one of the best known algorithms used in clustering; nevertheless it has many disadvantages as it may converge to a local optimum, depending on its random initialization of prototypes. We will propose an enhancement to the initialization process of k-means, which depends on using statistical information from the data set to initialize the prototypes. We show that our algorithm gives valid clusters, and that it decreases error and time.

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Hierarchical initialization approach for K-Means clustering

Cited by 71 publications

References 5 publications

Face Extraction from Image based on K-Means Clustering Algorithms

Face Extraction from Image based on K-Means Clustering Algorithms

A New Initialization Method to Originate Initial Cluster Centers for K-Means Algorithm

Initializing KMeans Clustering Algorithm using Statistical Information

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