Evaluation of Fuzzy Clustering

Sato‐Ilic, Mika; Jain, Lakhmi C.

doi:10.1007/3-540-34357-1_5

Cited by 4 publications

(6 citation statements)

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“…But this requirement does not reflect the uncertainty in membership inherent in real situations and does not allow depiction of compositional transition from one state to another. Fuzzy clustering measures the degree of compositional membership (Sato‐Ilic and Jain ) of an individual site to a particular vegetation community: a site may have any real membership value within the interval [0, 1] (Equihua ). Sites may therefore have varying degrees of membership simultaneously to a number of different vegetation communities.…”

Section: Methodsmentioning

confidence: 99%

Detecting state changes for ecosystem conservation with long‐term monitoring of species composition

Mason

Keith

Letten

2017

Ecological Applications

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Effective conservation requires an understanding not only of contemporary vegetation distributions in the landscape, but also cognizance of vegetation transitions over time with the goal of maintaining persistence of all states within the landscape. Using a state and transition model framework, we investigated temporal transitions over 31 years in species composition among five upland swamp vegetation communities in southeastern Australia. We applied fuzzy clustering to document transitions across communities; evaluated the resilience and resistance of communities to change; and explored the relationship between ecosystem states and major environmental factors posited to structure the system. We also evaluated the predictive ability of an established vegetation dynamics model. We found that community composition remained stable or underwent reversible or directional transitions depending on the vegetation type. Wetter communities (Ti-tree thicket and Cyperoid heath) were more stable (i.e., resistant) while drier communities showed a greater propensity to transition (i.e., had lower resistance) under the observed disturbance regime (low variance fire intervals). The resilience of drier communities differed under this regime, with Banksia thicket showing reversible compositional change, while Restioid heath and Sedgeland showed directional change. In accord with an established conceptual model, we found that communities were distributed along a hydrological gradient. In addition, vegetation structure, along with light penetration to ground level, differentiated communities. However, internal dynamics of drier communities were complex: differences in fire regime (penultimate fire interval in 2014 and number of fires since 1965) were unable to predict differences in community membership among sites. Aspects of the fire regime are expected to be more important predictors if fire intervals vary more strongly among sites in the future. Fuzzy clustering of compositional data allows managers to track community transitions over time and facilitates planned interventions for conservation purposes.

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Section: Methodsmentioning

confidence: 99%

Detecting state changes for ecosystem conservation with long‐term monitoring of species composition

Mason

Keith

Letten

2017

Ecological Applications

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“…The most widely used soft clustering algorithm is the FCM. The aim is to minimize the following objective function [67,68]:…”

Section: Description Of the Algorithmmentioning

confidence: 99%

“…Also, soft clustering is beneficial in cases where the distinctions margins of the generated clusters in the feature space are vague. The advantages of soft clustering have been shown in a wide diversity of research fields [67,68].…”

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confidence: 99%

An Improved Fuzzy C-Means Algorithm for the Implementation of Demand Side Management Measures

et al. 2017

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Abstract:Load profiling refers to a procedure that leads to the formulation of daily load curves and consumer classes regarding the similarity of the curve shapes. This procedure incorporates a set of unsupervised machine learning algorithms. While many crisp clustering algorithms have been proposed for grouping load curves into clusters, only one soft clustering algorithm is utilized for the aforementioned purpose, namely the Fuzzy C-Means (FCM) algorithm. Since the benefits of soft clustering are demonstrated in a variety of applications, the potential of introducing a novel modification of the FCM in the electricity consumer clustering process is examined. Additionally, this paper proposes a novel Demand Side Management (DSM) strategy for load management of consumers that are eligible for the implementation of Real-Time Pricing (RTP) schemes. The DSM strategy is formulated as a constrained optimization problem that can be easily solved and therefore, making it a useful tool for retailers' decision-making framework in competitive electricity markets.

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“…In the present study, among the three techniques for hydrological data clustering listed by Jingyi and Hall (2004), it was used the third method, fuzzy c-means (fcm). The fcm is an algorithm based on fuzzy logic, which aims to establish the similarities that a sample data shares with each cluster (BEZDEK et al, Steffen and Gomes 3/12 1984), and to represent the uncertainties inherent to the real data (SATO-ILIC;JAIN, 2006).…”

Section: Introductionmentioning

confidence: 99%

“…The concept of clustering based on fuzzy logic can be better understood when it is compared to the classical definition of clustering. According to Sato-Ilic and Jain (2006), in the traditional approach, when a sample data is clustered, it belongs only to one cluster, without sharing similar characteristics to the others. In clustering based on fuzzy logic, however, Sato-Ilic and Jain (2006) affirm that each sample data present distinct degrees of similarity with each cluster.…”

Section: Introductionmentioning

confidence: 99%

Clustering of historical floods observed on Iguaçu River, in União da Vitória, Paraná

Steffen

Gomes

2018

RBRH

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The occurrence of flood events has become more frequent, and, in Brazil, there are regions that suffer with the repetition of those events. In União da Vitória, located in Paraná State, Brazil, those phenomena are commonly recorded, generating series of consequences for each flood event, such as financial losses, environmental damages, home losses and deaths. So, since it is not possible to avoid the occurrence of floods, it is necessary to reduce its impacts, and in a scenario of repeated flood events, as observed in União da Vitória, it is justified the clustering of historical floods, aiming to improve the knowledge about the river basin hydrological behavior and to assist in the determination of hydrological models parameters. Clustering analysis aims to establish sets of events with similar characteristics, and, for this, based on fuzzy logic, the present study uses the fuzzy c-means method to cluster Iguaçu river floods, observed in União da Vitória, using a set of different flood severity indicators. The classification defined four clusters, according to different flood severity levels, so-called: low; medium; high; and, disaster or catastrophe. Therefore, by the analysis of similar features among different clusters of events, it is further possible to study the flood formation mechanisms, contributing to the reduction of its impacts, through real-time flood alert and forecasting systems, for instance.

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Evaluation of Fuzzy Clustering

Cited by 4 publications

References 0 publications

Detecting state changes for ecosystem conservation with long‐term monitoring of species composition

Detecting state changes for ecosystem conservation with long‐term monitoring of species composition

An Improved Fuzzy C-Means Algorithm for the Implementation of Demand Side Management Measures

Clustering of historical floods observed on Iguaçu River, in União da Vitória, Paraná

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