Solving Non-Uniqueness in Agglomerative Hierarchical Clustering Using Multidendrograms

Fernández, Alberto; Gómez, Sergio

doi:10.1007/s00357-008-9004-x

Cited by 129 publications

(96 citation statements)

References 12 publications

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“…In this section, we construct dendogram "maps" [6,16]. Our starting point will be the set of experimental SC measures handled by indices (2)-(3) and (5)- (6).…”

Section: Dendogramsmentioning

confidence: 99%

“…Our starting point will be the set of experimental SC measures handled by indices (2)-(3) and (5)- (6). The resulting matrix C = [c jk ] is treated by the package MultiDendograms hierarchical clustering package [16], and the results are visualized in Figs.…”

Section: Dendogramsmentioning

confidence: 99%

“…As described previously, the starting point will be the matrix C = [c jk ] based on indices (2)- (3) and (5)- (6). Figures 5 and 6 depict the MDS maps for two alternative indices.…”

Section: Multidimensional Scalingmentioning

confidence: 99%

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Analysis of UV spectral bands using multidimensional scaling

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Băleanu

2013

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This study describes the change of the ultravi-olet spectral bands starting from 0.1 to 5.0 nm slit width in the spectral range of 200-400 nm. The analysis of the spectral bands is carried out by using the multidimensional scaling (MDS) approach to reach the latent spectral back-ground. This approach indicates that 0.1 nm slit width gives higher-order noise together with better spectral details. Thus, 5.0 nm slit width possesses the higher peak amplitude and lower-order noise together with poor spectral details. In the above-mentioned conditions, the main problem is to find the relationship between the spectral band properties and the slit width. For this aim, the MDS tool is to used recognize the hidden information of the ultraviolet spectra of sildenafil cit-rate by using a Shimadzu UV-VIS 2550, which is in theworld the best double monochromator instrument. In this study, the proposed mathematical approach gives the rich findings for the efficient use of the spectrophotometer in the qualitative and quantitative studies.

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“…In this section, we construct dendogram "maps" [6,16]. Our starting point will be the set of experimental SC measures handled by indices (2)-(3) and (5)- (6).…”

Section: Dendogramsmentioning

confidence: 99%

Section: Dendogramsmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of UV spectral bands using multidimensional scaling

Machado

Dinç

Băleanu

2013

SIViP

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“…Agglomerative or 'bottom up' approaches (Ward 1963, Fernandez andGomez 2008) start with each object as a cluster and recursively merges two clusters with the most similarity. Divisive or 'top down' approaches (Chavent et al 2007, Zhong 2008) start with all observations as one cluster and at each step divides the cluster with the most dissimilar observations.…”

Section: Introductionmentioning

confidence: 99%

Ellipsoidal mixture models for clustering: an application to web-based education data

Jafreen

Mallick

Khan

2016

Bangladesh J. Sci. Res.

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This study considers the Gaussian mixture models for clustering. Since spherical and diagonal models occur very rarely in practice and analysis can be simplified when these models are implemented, we focus on the ellipsoidal models. EM algorithm is used to fit these models to a real data set related to an adaptive educational electronic course. Misclassification rates and Bayesian Information Criteria (BIC) values are used for comparison.

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“…Clustering has been extensively used in the fields of pattern recognition, statistics and machine learning. Typically, the most common algorithms are hierarchical clustering algorithm [1][2][3] and K-means algorithm [4]. Meanwhile, the clustering algorithm based on probability model [5,6] is becoming more and more widely applied.…”

Section: Introductionmentioning

confidence: 99%

Clustering Algorithm Based on Local Random Walkwith Distance Measure

2016

IJSR

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Cluster analysis is widely used in the field of data mining. However, the K-means algorithm which is widely used has a strong sensitivity for the initial values. Namely, the parameters such as clustering coefficient and centroid should be determined when the cluster is initialized. In the paper, we propose a K-means algorithm that based on link information and regard KL divergence distance as the objective function. This method not only introduces the way of the local random walk with the shortest path, but also uses the link information to convert the distance space. In other word, we utilize the local random walk with the shortest path to convert the distance between data into the transition probability of the random walk. Then, we use the random walk realize the conversion of the distance space. The core concept is the distance of converting node pair that refers to the node to the whole network node distance. The experimental results show that the proposed algorithm can improve the cluster result efficiently.

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Solving Non-Uniqueness in Agglomerative Hierarchical Clustering Using Multidendrograms

Cited by 129 publications

References 12 publications

Analysis of UV spectral bands using multidimensional scaling

Analysis of UV spectral bands using multidimensional scaling

Ellipsoidal mixture models for clustering: an application to web-based education data

Clustering Algorithm Based on Local Random Walkwith Distance Measure

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