A Clustering Method Based on K-Means Algorithm

Li, You Guo

doi:10.4028/www.scientific.net/amm.380-384.1697

Cited by 34 publications

(33 citation statements)

References 2 publications

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“…Each clustering method is built on a specific criterion to divide samples into groups; in general, these similarities between instances are measured via distance calculation methods [7]. For example, k-means clustering, which is one of the most commonly employed clustering methods because of its effectivity [8][9][10], is based on dividing samples into k different groups in which the intracluster similarities are maximized and the intercluster similarities are minimized [6]. This is achieved by an iterative process for determining optimal clustering centers and in this iterative process similarities are calculated based on the square error criterion [8].…”

Section: Introductionmentioning

confidence: 99%

A novel approach for extracting ideal exemplars by clustering for massivetime-ordered datasets

Ertuğrul¹

2017

Turk J Elec Eng & Comp Sci

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Abstract:The number and length of massive datasets have increased day by day and this yields more complex machine learning stages due to the high computational costs. To decrease the computational cost many methods were proposed in the literature such as data condensing, feature selection, and filtering. Although clustering methods are generally employed to divide samples into groups, another way of data condensing is by determining ideal exemplars (or prototypes), which can be used instead of the whole dataset. In this study, first the efficiency of traditional data condensing by clustering approach was confirmed according to obtained accuracies and condensing ratios in 9 different synthetic or real batch datasets. This approach was then improved to be employed in time-ordered datasets. In order to validate the proposed approach, 23 different real time-ordered datasets were used in experiments. Achieved mean RMSEs were 0.27 and 0.29 by employing the condensed (mean condensed ratio was 97.17%) and the whole datasets, respectively. Obtained results showed that higher accuracy rates and condensing ratios were achieved by the proposed approach.

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Section: Introductionmentioning

confidence: 99%

A novel approach for extracting ideal exemplars by clustering for massivetime-ordered datasets

Ertuğrul¹

2017

Turk J Elec Eng & Comp Sci

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“…The amount of scenarios that are out of the confidence interval in some hour could be easily controlled using the factor ; whether is fixed to 0.7 those scenarios with values of equal or higher than are selected. Then those scenarios to be considered in stochastic UC problem are chosen by means of k-means clustering algorithm [14]. Once, wind speed scenarios have been generated, the corresponding wind power production is calculated by using the modelling of the power curve of a single wind turbine presented in (5) [15]:…”

Section: Methodology To Scenario Generationmentioning

confidence: 99%

“…These limitations are modeled including minimum up time ( ) and minimum down time ( ) of generator . This constraint is presented in (14):…”

Section: F Minimum Up and Down Time Constraintsmentioning

confidence: 99%

Fast Method to the Unit Scheduling of Power Systems with Renewable Power Sources

Osório¹,

Lujano-Rojas²,

Matias³

et al. 2024

RE&PQJ

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Abstract. Modelling wind power uncertainty is a critic aspect in the optimal management of power systems with high integration of this renewable resource. It is typically carried out by considering a limited number of representative scenarios that incorporate relevant properties such as hourly auto-correlation and diurnal forecasting profile. Considering a large amount of scenarios improves the wind power modelling, but increases the computational effort. To deal with this problem, a method to incorporate a big set of scenarios in stochastic unit commitment (UC) problem is presented in this paper. The effectiveness of the proposed methodology is evaluated by means of the analysis of a case study and the results are compared to those obtained from a stochastic programming method, concluding that the method presented in this paper offers an approximated solution in a reduced computational time.

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“…Business houses use clustering to conceive and distinguish interests of their customers based on purchasing pattern and delineate the categories of customers [2].…”

Section: Introductionmentioning

confidence: 99%

A Study on Outlier distance and SSE with multidimensional datasets in K-means clustering

Rajee

Francis

2013

2013 Fifth International Conference on Advanced Computing (ICoAC)

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Clustering is a very well-known technique in data mining. One of the most widely used clustering techniques is the K-means algorithm. It is very popular because it is conceptually simple, computationally fast and memory efficient. In this paper, the role of noise points in limiting the efficacy of k-means algorithm was presented, by analyzing them within the purview of sum-of-squared error (SSE), which continues to remain the undisputedly popular validation method of K-means algorithm. Experimental studies were made with synthetic data sets of multiple dimensions and cluster sizes. Numerous noise points were barraged to the K clusters and the effect of noise distance on SSE was considered. On analyzing the results, we infer that the distance of noise to the cluster center influences SSE.This correlative study holds much significance, as the k means algorithm assumes that the number of clusters in the database is perceived in anticipation. Apparently, this is not necessarily true in real-world applications. The study probes the pathognomonic role of noise points in the clustering outcome, which in the process will serve to provide with better results in real-world applications.

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A Clustering Method Based on K-Means Algorithm

Cited by 34 publications

References 2 publications

A novel approach for extracting ideal exemplars by clustering for massivetime-ordered datasets

A novel approach for extracting ideal exemplars by clustering for massivetime-ordered datasets

Fast Method to the Unit Scheduling of Power Systems with Renewable Power Sources

A Study on Outlier distance and SSE with multidimensional datasets in K-means clustering

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