Soft clustering – Fuzzy and rough approaches and their extensions and derivatives

Peters, Georg; Crespo, Fernando; Lingras, Pawan; Weber, Richard W.

doi:10.1016/j.ijar.2012.10.003

Cited by 160 publications

(82 citation statements)

References 77 publications

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“…Although widely used in other different fields such as engineering, bio-informatics and marketing [34], these clustering techniques are also applied by several authors, e.g., [36][37][38], with the aim of classifying groups of trajectories based on similarity criteria for example cyclogenesis/cyclolysis locations, trajectory, mean intensity, among others. Thus, in order to achieve that, the K-means clustering technique was applied.…”

Section: Clustering Methodology For Trajectories Analysismentioning

confidence: 99%

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Wind Power Ramps Driven by Windstorms and Cyclones

2017

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Abstract:The increase in the wind power predictability assumes a very important role for secure power system operation at minimum costs, especially in situations with severe changes in wind power production. In order to improve the forecast of such events, also known as "wind power ramp events", the underlying role of some severe meteorological phenomena in triggering wind power ramps must be clearly understood. In this paper, windstorm and cyclone detection algorithms are implemented using historical reanalysis data allowing the identification of key characteristics (e.g., location, intensity and trajectories) of the events with the highest impact on the wind power ramp events in Portugal. The results show a strong association between cyclones/windstorms and wind power ramp events. Moreover, the results highlight that it is possible to use some features of these meteorological phenomena to detect, in an early stage, severe wind power ramps thus creating the possibility to develop operational decision tools in order to support the security of power systems with high amounts of wind power generation.

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Section: Clustering Methodology For Trajectories Analysismentioning

confidence: 99%

“…In order to deploy a methodology capable of detecting wind power ramps, some definitions found in the literature were considered. A detailed description of these methodologies can be found in [10,33,34].…”

Section: Ramp Definitionmentioning

confidence: 99%

Wind Power Ramps Driven by Windstorms and Cyclones

2017

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“…Contrary to classical (hard) partitional clustering, in which each object is assigned unambiguously and with full certainty to a single cluster, these variants allow for ambiguity, uncertainty or doubt in the assignment of objects to clusters. For this reason, they are referred to as "soft" clustering methods [28], in contrast with classical, "hard" clustering. Among soft clustering paradigms, evidential clustering describes the uncertainty in the membership of each object to clusters using a Dempster-Shafer mass function [30], which assigns a mass to each subset of clusters.…”

Section: Introductionmentioning

confidence: 99%

Evidential clustering of large dissimilarity data

Denźux

Sriboonchitta

Kanjanatarakul

2016

Knowledge-Based Systems

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In evidential clustering, the membership of objects to clusters is considered to be uncertain and is represented by Dempster-Shafer mass functions, forming a credal partition. The EVCLUS algorithm constructs a credal partition in such a way that larger dissimilarities between objects correspond to higher degrees of conflict between the associated mass functions. In this paper, we present several improvements to EVCLUS, making it applicable to very large dissimilarity data. First, the gradient-based optimization procedure in the original EVCLUS algorithm is replaced by a much faster iterative row-wise quadratic programming method. Secondly, we show that EVCLUS can be provided with only a random sample of the dissimilarities, reducing the time and space complexity from quadratic to roughly linear. Finally, we introduce a two-step approach to construct credal partitions assigning masses to selected pairs of clusters, making the algorithm outputs more informative than those of the original EVCLUS, while remaining manageable for large numbers of clusters.

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“…Any pattern may belong to one and only one class in this case. In the case of fuzzy clustering, a pattern may belong to all the classes with a certain fuzzy membership grade (Jain, 2010;Pedrycz & Rai, 2008;Peters et al, 2013). Hierarchical clustering algorithms iteratively build clusters by joining (agglomerative) or dividing (divisive) the clusters from the previous iteration (Kannappan et al, 2011;Chehreghani et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Comparing clustering models in bank customers: Based on Fuzzy relational clustering approach

Hendalianpour¹,

Razmi²,

Gheitasi³

2017

10.5267/j.ac

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Clustering is absolutely useful information to explore data structures and has been employed in many places. It organizes a set of objects into similar groups called clusters, and the objects within one cluster are both highly similar and dissimilar with the objects in other clusters. The K-mean, C-mean, Fuzzy C-mean and Kernel K-mean algorithms are the most popular clustering algorithms for their easy implementation and fast work, but in some cases we cannot use these algorithms. Regarding this, in this paper, a hybrid model for customer clustering is presented that is applicable in five banks of Fars Province, Shiraz, Iran. In this way, the fuzzy relation among customers is defined by using their features described in linguistic and quantitative variables. As follows, the customers of banks are grouped according to K-mean, C-mean, Fuzzy C-mean and Kernel K-mean algorithms and the proposed Fuzzy Relation Clustering (FRC) algorithm. The aim of this paper is to show how to choose the best clustering algorithms based on density-based clustering and present a new clustering algorithm for both crisp and fuzzy variables. Finally, we apply the proposed approach to five datasets of customer's segmentation in banks. The result of the FCR shows the accuracy and high performance of FRC compared other clustering methods.Growing Science Ltd. All rights reserved. 7

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Soft clustering – Fuzzy and rough approaches and their extensions and derivatives

Cited by 160 publications

References 77 publications

Wind Power Ramps Driven by Windstorms and Cyclones

Wind Power Ramps Driven by Windstorms and Cyclones

Evidential clustering of large dissimilarity data

Comparing clustering models in bank customers: Based on Fuzzy relational clustering approach

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