A fast -means clustering algorithm using cluster center displacement

Lai, Jim Z. C.; Huang, Tsung-Jen; Liaw, Yi‐Ching

doi:10.1016/j.patcog.2009.02.014

Cited by 77 publications

(30 citation statements)

References 10 publications

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“…It is the improvement of the algorithm in [12]. A fast K-means clustering algorithm named FKMCUCD was proposed in [15] using cluster center displacement. This method is significant for highdimensional large data.…”

Section: Literature Surveymentioning

confidence: 99%

HK-Means: A Heuristic Approach to Initialize and Estimate the Number of Clusters in Biological Data

2016

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K-means algorithm is one of the simplest and fastest clustering algorithms existing since more than four decades. One of the limitations of this algorithm is estimating number of clusters in advance. This algorithm also suffers from random initialization problem. This paper proposes a heuristic which initializes the cluster centers and estimates the number of clusters as a discrete value. The method estimates the number of clusters and initializes many cluster centers successfully for the clusters that are dense and separated significantly. The method selects a new cluster center in each iteration. The point selected is the point which is most dissimilar from the previously chosen points. The proposed algorithm is experimented on various synthetic data and the results are encouraging.

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Section: Literature Surveymentioning

confidence: 99%

HK-Means: A Heuristic Approach to Initialize and Estimate the Number of Clusters in Biological Data

2016

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“…In this study, the k-means clustering algorithm was used for the detection of stable and unstable areas and a clustering algorithm used to determine the GNSS deformation vector values. The k-means clustering algorithm, which is also called the generalised Lloyd algorithm, is a special case of the generalised hard clustering scheme, wherein point representatives are adopted and Euclidean distances used to measure the dissimilarity between a vector X and its cluster representative C (Lai et al 2009;Wan 2012).…”

Section: Gnss Data Collection and Analysismentioning

confidence: 99%

Monitoring landslides with geophysical and geodetic observations

2015

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The objective of this study was to evaluate and predict land movement by integrating geodetic, geophysical and meteorological data in a landslide area. Specifically, electrical resistivity tomography surveying, Global Navigation Satellite System and terrestrial laser scanning techniques were integrated to monitor a landslide. The study area lies to the southeast of the town of Taşkent in southern Turkey, close to Balcılar in the Central Taurus mountain chain. Landslides result in considerable damage to structures, farmland and the environment in this area; therefore, it is important to characterise the size, extent and timing of past land movements in order to mitigate damage from future landslides. Analysis presented in this paper shows that the greatest land movements in the region occur in spring, when average motions can be up to 1.5 m per month. It is demonstrated that integrated techniques provide a better means for monitoring landslide processes and gathering data for predictions of future movements. Mapping landslide movements by integrating geophysical and geodetic observations can provide a meaningful evaluation of a landslide and its dynamics.

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“…Lai et al [4] proposed fast k-means clustering algorithm using the displacements of cluster centers to reject unlikely candidates for a data point. The computing time of the proposed algorithm increases linearly with the data dimension d, whereas the computational complexity of major available kd-tree based algorithms increases exponentially with the value of d. Theoretical analysis shows that the method can reduce the computational complexity of full search by a factor of SF and SF is independent of vector dimension.…”

Section: Literature Reviewmentioning

confidence: 99%

Pattern discovery using k-means algorithm

Ahmed

Ishak

Norwawi

et al. 2014

2014 World Congress on Computer Applications and Information Systems (WCCAIS)

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Abstract-Student's placement in industry for the industrial training is difficult due to the large number of students and organizations involved. Further the matching process is complex due to the various criteria set by the organization and students. This paper will discuss the results of a pattern extraction process using a clustering algorithm that is k-means. The data use consists of Bachelor of Information Technology and Bachelor in Multimedia students of Universiti Utara Malaysia from the year 2004 till 2005. The experiments were conducted using undirected data and directed data. The pattern extracted gave information on the previous matching process done by the university.

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A fast -means clustering algorithm using cluster center displacement

Cited by 77 publications

References 10 publications

HK-Means: A Heuristic Approach to Initialize and Estimate the Number of Clusters in Biological Data

HK-Means: A Heuristic Approach to Initialize and Estimate the Number of Clusters in Biological Data

Monitoring landslides with geophysical and geodetic observations

Pattern discovery using k-means algorithm

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