Clustering Analysis

Diday, Edwin; Simon, Jean Claude

doi:10.1007/978-3-642-67740-3_3

Cited by 83 publications

(82 citation statements)

References 21 publications

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“…The partitioning algorithm we use here, after Michelangeli et al (1995), is known as the dynamical cluster algorithm (Diday and Simon, 1976). It does not suffer from the known drawbacks of the Ward methodology.…”

Section: The Dynamical Cluster Algorithmmentioning

confidence: 99%

The impact of El Niño–southern oscillation upon weather regimes over Europe and the North Atlantic during boreal winter

Moron

Plaut

2003

Intl Journal of Climatology

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The influence of the warm and cold sea-surface temperatures in the eastern and central equatorial Pacific associated with El Niño-southern oscillation (ENSO) on the probability of occurrence of weather regimes (WRs) over the North Atlantic sector is investigated for the period November-March. Five WRs are identified from daily sea-level pressure anomalies (SLPAs) during 119 winters over this sector by applying cluster analysis: the positive North Atlantic oscillation (NAO; called ZO for zonal) and negative NAO (called WBL for west blocking) patterns; GA (for Greenland anticyclone), with a positive SLPA shifted north of 60°N; EA (for European anticyclone) with a positive SLPA over Europe but enhanced north-south SLPA gradient over the western and central North Atlantic; and AR (for Atlantic Ridge) with a positive (negative) SLPA over the central North Atlantic (northern and central Europe).El Niño winters are associated with a significant increase (decrease) in the prevalence of ZO (WBL) in November-December and a significant increase (decrease) in the prevalence of GA and WBL (EA and ZO)

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Section: The Dynamical Cluster Algorithmmentioning

confidence: 99%

The impact of El Niño–southern oscillation upon weather regimes over Europe and the North Atlantic during boreal winter

Moron

Plaut

2003

Intl Journal of Climatology

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“…Here, simplicity is either from the perspective of automatic analysis (so that a machine can perform further processing efficiently) or it is human-oriented (so that the representation obtained is easy to comprehend and intuitively appealing). In the clustering context, a typical data abstraction is a compact description of each cluster, usually in terms of cluster prototypes or representative patterns such as the centroid [5].…”

Section: B Components Of Clustering Taskmentioning

confidence: 99%

State of Art of Different Clustering Approaches

Rawlani¹,

Natekar²,

Pathak³

et al. 2015

International Journal of Advanced Research in Computer and Comm

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This Paper presents an overview of the clustering and its methods used in Data Mining. Firstly, different measures that are used for determining whether two clusters are similar or dissimilar are defined. Then different methods of clustering are presented and are divided into: hierarchical, partitional and evolutionary algorithms. Finally clustering is performed in large data sets and subsequently their challenges are discussed.

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“…We will use here the special case of k-means clustering which has been proposed by Diday and Simon (1976) and Bock (1989) and belongs to the family of alternated least squares techniques:…”

Section: Clusterwise Linear Regressionmentioning

confidence: 99%

“…This section presents a clusterwise regression model based on both the dynamic clustering algorithm (Diday and Simon (1976)) and the center and range regression model for interval-valued data (Lima Neto and De Carvalho (2008)). …”

Section: Clusterwise Regression On Interval-valued Datamentioning

confidence: 99%

A Clusterwise Center and Range Regression Model for Interval-Valued Data

Carvalho

Saporta

Queiroz

2010

Proceedings of COMPSTAT'2010

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Abstract. This paper aims to adapt clusterwise regression to interval-valued data. The proposed approach combines the dynamic clustering algorithm with the center and range regression method for interval-valued data in order to identify both the partition of the data and the relevant regression models, one for each cluster. Experiments with a car interval-valued data set show the usefulness of combining both approaches.

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Clustering Analysis

Cited by 83 publications

References 21 publications

The impact of El Niño–southern oscillation upon weather regimes over Europe and the North Atlantic during boreal winter

The impact of El Niño–southern oscillation upon weather regimes over Europe and the North Atlantic during boreal winter

State of Art of Different Clustering Approaches

A Clusterwise Center and Range Regression Model for Interval-Valued Data

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