What to Do When K-Means Clustering Fails: A Simple yet Principled Alternative Algorithm

Raykov, Yordan P.; Boukouvalas, Alexis; Baig, Fahd; Little, Max A.

doi:10.1371/journal.pone.0162259

Cited by 149 publications

(100 citation statements)

References 43 publications

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“…The features of demand are the day types (weekday or weekend) and number of residents, while those for PV include the PV capacity, panel orientation and weather information. Clustering is completed using either k-means clustering [15] or maximum a-posteriori Dirichlet process mixtures (MAP-DP) clustering [16], which is useful for instances in which the number of clusters cannot be easily determined.…”

Section: A Feature-based Cluster Assignmentmentioning

confidence: 99%

A Nonparametric Bayesian Methodology for Synthesizing Residential Solar Generation and Demand Data

Power

Verbič

Chapman

2020

IEEE Trans. Smart Grid

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The uptake of behind-the-meter distributed energy resources in low-voltage distribution networks has reached a level where network issues have started to emerge, which requires new tools for operation and planning. In this paper, we propose a methodology for synthesizing stochastic demand and generation profiles for unobserved customers with rooftop PV, called prosumers. The proposed model bridges the gap between the limited available empirical data, and the large amount of high-quality, stochastic demand and generation data required for probabilistic analysis. The approach employs clustering analysis and a Dirichlet-categorical hierarchical model of the features of unobserved prosumers. Based on the data of clusters of prosumers, Markov chain models of demand and generation profiles are constructed from empirical data, and synthetic demand profiles are subsequently sampled from these. The sampled traces are cross-validated and show a good statistical fit to the observed data. Two case studies are considered to confirm the validity of the proposed methodology. The first studies the impact of behavioral differences on the synthetic demand profiles, while the second looks at the impact of varying solar generation penetration on demand profiles.

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Section: A Feature-based Cluster Assignmentmentioning

confidence: 99%

A Nonparametric Bayesian Methodology for Synthesizing Residential Solar Generation and Demand Data

Power

Verbič

Chapman

2020

IEEE Trans. Smart Grid

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“…Joaquín Pérez-Ortega 1 *, Nelva Nely Almanza-Ortega 1 , Andrea Vega-Villalobos 1 , Rodolfo Pazos-Rangel 2 , Crispín Zavala-Díaz 3…”

Section: Author Detailsunclassified

“…The accelerated progress of technology in recent time is fostering an important increase in the amount of generated and stored data [1][2][3][4] in fields such as engineering, finance, education, medicine, and commerce, among others. Therefore, there is justified interest in obtaining useful knowledge that can be extracted from those huge amounts of data, in order to help making better decisions and understanding the nature of data.…”

Section: Introductionmentioning

confidence: 99%

TheK-Means Algorithm Evolution

Pérez-Ortega¹,

Almanza-Ortega²,

Vega-Villalobos³

et al. 2020

Introduction to Data Science and Machine Learning

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Clustering is one of the main methods for getting insight on the underlying nature and structure of data. The purpose of clustering is organizing a set of data into clusters, such that the elements in each cluster are similar and different from those in other clusters. One of the most used clustering algorithms presently is K-means, because of its easiness for interpreting its results and implementation. The solution to the K-means clustering problem is NP-hard, which justifies the use of heuristic methods for its solution. To date, a large number of improvements to the algorithm have been proposed, of which the most relevant were selected using systematic review methodology. As a result, 1125 documents on improvements were retrieved, and 79 were left after applying inclusion and exclusion criteria. The improvements selected were classified and summarized according to the algorithm steps: initialization, classification, centroid calculation, and convergence. It is remarkable that some of the most successful algorithm variants were found. Some articles on trends in recent years were included, concerning K-means improvements and its use in other areas. Finally, it is considered that the main improvements may inspire the development of new heuristics for K-means or other clustering algorithms.

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“…Современные исследователи [36,37] отмечают, что кластеризация данных -сложная задача, предполагающая выбор между различными методами, параметрами и показателями эффективности. Следовательно, особое внимание необходимо уделять выбору алгоритма кластеризации.…”

Section: методы исследованияunclassified

Forest fire hazard assessment by clustering and using neural networks under unstability and uncertainty

Stankevich

2020

Vestnik of Astrakhan State Technical University. Series: Management, Computer Science and Informatics

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The paper focuses on the data on forest fires and identification of key natural and anthropogenic factors that are crucial for forest management, especially, for developing and implementing the fire safety measures. In recent decades, there have been observed the increased environmental, social and economic losses from the forest fires on a global scale, which has required stepped-up fire-fighting surveillance, especially in the preventive forest fire risk assessment. In all the variety of modern approaches aimed at assessing the fire hazards to the forests and taking into account the effecting environmental factors, most of them are based on simplified calculations and do not take into account different factors, mainly anthropological ones. The purpose of the study is to assess the forest fire risk depending on the environmental factors by using cluster analysis in conditions of instability and uncertainty. It could help applying the integrated approach to forest fire risk assessing in order to take into account both natural and anthropogenic factors in difficult conditions. To assess the forest fire risk, there were used the data obtained by MODIS spectroradiometer from January 1, 2014 to November 24, 2019: latitude; longitude; acquisition time and date. The following parameters were used as additional: Fire Weather Index; minimum distance to an inhabited locality; minimum distance to the road (highway or railway); minimum distance to the water area; holiday / day off; potential value. According to the results of the spatial distribution of forest fires and taking into account the data on the environmental factors there have been formed three clusters; there has been revealed a key relationship between the probability of a forest fire and proximity to the inhabited locality. There has been submitted the index of forest fire risk assessment (the Fire Weather and Human Index (FWHI)) based on the natural and anthropogenic impacts. Identification of social and biophysical aspects of the community exposure to fires and the adaptation of the existing fire prevention strategy will improve the forest fire safety system.

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What to Do When K-Means Clustering Fails: A Simple yet Principled Alternative Algorithm

Cited by 149 publications

References 43 publications

A Nonparametric Bayesian Methodology for Synthesizing Residential Solar Generation and Demand Data

A Nonparametric Bayesian Methodology for Synthesizing Residential Solar Generation and Demand Data

TheK-Means Algorithm Evolution

Forest fire hazard assessment by clustering and using neural networks under unstability and uncertainty

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