Practical Problems in a Method of Cluster Analysis

Marriott, F. H. C.

doi:10.2307/2528592

Cited by 192 publications

(73 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Cindex (Hubert and Levin 1976) × × 10 DB (Davies and Bouldin 1979) × × × 11 Ratkowsky (Ratkowsky and Lance 1978) × 12 Scott (Scott and Symons 1971) × 13 Marriot (Marriot 1971) × 14 Ball (Ball and Hall 1965) × 15 Trcovw (Milligan and Cooper 1985) × 16 Tracew (Milligan and Cooper 1985) × 17 Friedman (Friedman and Rubin 1967) × 18 Rubin (Friedman and Rubin 1967) × 19 Dunn (Dunn 1974) × × Table 1: Indices implemented in SAS and R packages.…”

Section: Indexmentioning

confidence: 99%

See 1 more Smart Citation

NbClust: AnRPackage for Determining the Relevant Number of Clusters in a Data Set

Charrad¹,

Ghazzali²,

Boiteau³

et al. 2014

J. Stat. Soft.

2,496

1,621

View full text Add to dashboard Cite

Clustering is the partitioning of a set of objects into groups (clusters) so that objects within a group are more similar to each others than objects in different groups. Most of the clustering algorithms depend on some assumptions in order to define the subgroups present in a data set. As a consequence, the resulting clustering scheme requires some sort of evaluation as regards its validity.The evaluation procedure has to tackle difficult problems such as the quality of clusters, the degree with which a clustering scheme fits a specific data set and the optimal number of clusters in a partitioning. In the literature, a wide variety of indices have been proposed to find the optimal number of clusters in a partitioning of a data set during the clustering process. However, for most of indices proposed in the literature, programs are unavailable to test these indices and compare them.The R package NbClust has been developed for that purpose. It provides 30 indices which determine the number of clusters in a data set and it offers also the best clustering scheme from different results to the user. In addition, it provides a function to perform k-means and hierarchical clustering with different distance measures and aggregation methods. Any combination of validation indices and clustering methods can be requested in a single function call. This enables the user to simultaneously evaluate several clustering schemes while varying the number of clusters, to help determining the most appropriate number of clusters for the data set of interest.

show abstract

Section: Indexmentioning

confidence: 99%

“…"scott" (Scott and Symons 1971) Maximum difference between hierarchy levels of the index 16. "marriot" (Marriot 1971) Max. value of second differences between levels of the index 17.…”

Section: Name Of the Index In Nbclustmentioning

confidence: 99%

NbClust: AnRPackage for Determining the Relevant Number of Clusters in a Data Set

Charrad¹,

Ghazzali²,

Boiteau³

et al. 2014

J. Stat. Soft.

2,496

1,621

View full text Add to dashboard Cite

show abstract

“…We then took the determinant of withingroup covariance matrix as ͉W͉, with the smallest ͉W͉ indicating the best partitioning strategy (Marriott, 1971;Krzanowski and Lai, 1988). The first partition (three groups) resulted in the smallest ͉W͉ (2.04), whereas the ͉W͉ for the second partition (two groups) was 4.35 and ͉W͉ for the third partition (one group) was 18.25.…”

Section: Characteristics Of Motor Programs Elicited By Cbi-4 and Clusmentioning

confidence: 99%

The Construction of Movement with Behavior-Specific and Behavior-Independent Modules

Jing¹,

Cropper²,

Hurwitz³

et al. 2004

J. Neurosci.

121

View full text Add to dashboard Cite

Growing evidence suggests that different forms of complex motor acts are constructed through flexible combinations of a small number of modules in interneuronal networks. It remains to be established, however, whether a module simply controls groups of muscles and functions as a computational unit for use in multiple behaviors (behavior independent) or whether a module controls multiple salient features that define one behavior and is used primarily for that behavior (behavior specific). We used the Aplysia feeding motor network to examine the two proposals by studying the functions of identifiable interneurons. We identified three types of motor programs that resemble three types of behaviors that Aplysia produce: biting, swallowing, and rejection. Two ingestive programs (biting, swallowing) are defined by two movement parameters of the feeding apparatus (the radula): one is the same in both programs (phasing of radula closure motoneurons relative to radula protraction-retraction), whereas the other parameter (protraction duration) is different in the two programs. In each program, these two parameters were specified together by an individual neuron, but the neurons in each were different (B40 for biting, B30 for swallowing). These findings support the existence of behavior-specific modules. Furthermore, neuron B51 was found to mediate a phase that can be flexibly added on to both ingestive and egestive-rejection programs, suggesting that B51 may be a behavior-independent module. The functional interpretation of the role played by these modules is supported by the patterns of synaptic connectivity that they make. Thus, both behavior-specific and behavior-independent modules are used to construct complex behaviors.

show abstract

“…Although it is made different classifications in the literature (15,17,18), clustering analysis methods are separated into two main classes as hierarchical and nonhierarchical clustering methods according to the approaches in determining the clusters. In hierarchical clustering methods, pair in the number of n(n-1)/2 related to n unit is connected to each other hierarchically by means of forming dendrogram (tree graphic) considering the levels of similarity and distance between each other (4,21).…”

Section: Methodsmentioning

confidence: 99%

Classification of Karayaka and Bafra (Chios x Karayaka B1) sheep according to body measurements by different clustering methods

İbrahim;ÖZBEYAZ

2011

Ankara Üniversitesi Veteriner Fakültesi Dergisi

View full text Add to dashboard Cite

Summary:This study has been carried out to classify Karayaka and Bafra (Chios x Karayaka B 1 ) sheep according to body measurements by k-Means within nonhierarchical clustering methods and single linkage, average linkage and complete linkage within hierarchical clustering methods. In this research, age-adjusted data of body measurements of 223 Karayaka and 462 Bafra sheep in various ages breed in Gökhöyük Agricultural Enterprise that belongs to General Directorate of Agricultural Enterprises were used. At the end of the study, it was detected that it was obtained the best parameter value when Bafra sheep were divided into 7 clusters and Karayaka into 3 according to k-Means method, and in hierarchical clustering methods, it was obtained different clusters according to various levels of similarity and distance. These results put forward that according to classifications made by different clustering methods, Bafra has a more heterogeneous structure than Karayaka in terms of body measurements.

show abstract

Practical Problems in a Method of Cluster Analysis

Cited by 192 publications

References 10 publications

NbClust: AnRPackage for Determining the Relevant Number of Clusters in a Data Set

NbClust: AnRPackage for Determining the Relevant Number of Clusters in a Data Set

The Construction of Movement with Behavior-Specific and Behavior-Independent Modules

Classification of Karayaka and Bafra (Chios x Karayaka B1) sheep according to body measurements by different clustering methods

Contact Info

Product

Resources

About