Interval ckMeans: An algorithm for clustering symbolic data

Vargas, Rogério Rodrigues de; Bedregal, Benjamín R. C.

doi:10.1109/nafips.2011.5752042

Cited by 6 publications

(12 citation statements)

References 13 publications

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“…20 In this clustering study each data point (vector) contains all recorded noon time micorinverter temperatures corresponding to one of the eight brands. Thus every vector summarizes thermal behavior of the micorinverter throughout the window of the study.…”

Section: Clustering Analysismentioning

confidence: 99%

Thermal performance of microinverters on dual-axis trackers

Hossain

Peshek

et al. 2014

SPIE Proceedings

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Time-series insolation, environmental, thermal and power data were analyzed in a statistical analytical approach to identify the thermal performance of microinverters on dual-axis trackers under real-world operating conditions. This study analyzed 24 microinverters connected to 8 different brands of photovoltaic (PV) modules from July through October 2013 at the Solar Durability and Lifetime Extension (SDLE) SunFarm at Case Western Reserve University. Exploratory data analysis shows that the microinverter's temperature is strongly correlated with ambient temperature and PV module temperature, and moderately correlated with irradiance and AC power. Noontime data analysis reveals the variations of thermal behavior across different brands of PV module. Hierarchical clustering using the Euclidean distance measure principle was applied to noontime microinverter temperature data to group the similarly behaved microinverters. A multiple regression predictive model has been developed based on ambient temperature, PV module temperature, irradiance and AC power data to predict the microinverters temperature connected with different brands PV modules on dual-axis trackers.

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Section: Clustering Analysismentioning

confidence: 99%

Thermal performance of microinverters on dual-axis trackers

Hossain

Peshek

et al. 2014

SPIE Proceedings

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“…, otherwise (14) Therefore, the objective function presented in Eq. (15) and the calculation of the centers of the groups in Eq.…”

Section: Proposition 1: H B (Based In Boundaries Of Intervals) Is Hopmentioning

confidence: 99%

“…For interval-based similarity measures, for instance, recently the notion of interval-based distances were investigated on the studies as in [14] and [31]. However, All authors are with Informatics and Applied Mathematics Department, Federal University of Rio Grande do Norte (UFRN), Natal -RN, 59.072-970, Brazil.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy clustering algorithm with H-operator applied to problems with interval-based data

Silva

Moura

Canuto

et al. 2014

2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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The main advantage of using an interval-based distance for interval-based data lies on the fact that it preserves the underlying imprecision on intervals which is usually lost when real-valued distances are applied. One of the main problems when using interval-based distance in fuzzy clustering algorithms is the way to obtain the center of the groups. In this case, it is necessary to make adaptations in order to obtain those centers. Therefore, in this paper, we propose the use of the family of H-operator to proposed three approaches to transform the interval-based membership matrix into realvalued membership matrix and, as a consequence, to calculate the centers of the groups in interval-based fuzzy clustering algorithms. In this case, we will perform a comparative analysis using the three different approaches proposed in this paper, using seven interval-based datasets (four synthetic and three real datasets). As a result of this analysis, we will observe that the proposed approaches achieved better performance than all analyzed methods for interval-based methods.

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“…In which the authors proposed and theoretically described an arithmetic for data with interval nature. For interval-based similarity measures, for instance, recently the notion of interval-based distances were investigated in [16] and [37]. However, none of those studies provided a proper description on the subject.…”

Section: Introductionmentioning

confidence: 99%

An Interval-Based Framework for Fuzzy Clustering Applications

Silva

Moura

Canuto

et al. 2015

IEEE Trans. Fuzzy Syst.

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The main goal of using data with interval nature is to represent numeric information endowed with impreciseness, which are normally captured from measures of real world. However, in order to do this, it is necessary to adapt real-valued techniques to be applied on interval-based data. For intervalbased clustering applications, for instance, it is necessary to propose an interval-based distance and also to adapt clustering algorithms to be used in this context. Therefore, in this paper, we aim to provide a platform for performing clustering applications using interval-based data, including distance measure, clustering algorithms and validation indexes. In this case, we adapt an interval-based distance, called d km , and we propose two intervalbased fuzzy clustering algorithms, IbFcM (Interval-based FcM) and IbckM (Interval-based ckMeans), and three interval-based validation indexes. In order to validate the proposed intervalbased framework, an empirical analysis was conducted using seven clustering datasets, three real and four synthetic interval datasets. The empirical analysis is based on an external cluster validity index, Corrected Rand, and six internal-based validation indexes, in which three of them can be used in their original proposal and three are proposed in this paper. The obtained results show the usefulness of the proposed interval-based framework for interval-based clustering problems.

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Interval ckMeans: An algorithm for clustering symbolic data

Cited by 6 publications

References 13 publications

Thermal performance of microinverters on dual-axis trackers

Thermal performance of microinverters on dual-axis trackers

Fuzzy clustering algorithm with H-operator applied to problems with interval-based data

An Interval-Based Framework for Fuzzy Clustering Applications

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