Query: A dendrite method for cluster analysis

title: A dendrite method for cluster analysis
abstract: A method for identifying clusters of points in a multidimensional Euclidean space is described and its application to taxonomy considered. It reconciles, in a sense, two different approaches to the investigation of the spatial relationships between the points, viz. the agglomerative and the divisive methods. A graph, the shortest dendrite of Florek etal. (1951a) is constructed on a nearest neighbour basis and then divided into clusters by applying the criterion of minimum within cluster sum of squares. This procedure ensures an effective reduction of the number of possible splits. The method may be applied to a dichotomous division, but is perfectly suitable also for a global division into any number of clusters. An informal indicator of the "best number" of clusters is suggested. It is a"variance ratio criterion" giving some insight into the structure of the points. The method is illustrated by three examples, one of which is original. The results obtained by the dendrite method are compared with those.

title: "A Dendrite Method for Cluster Analysis" by Calinski and Harabasz: A Classical Work that is Far Too Often Incorrectly Cited
abstract: "A Dendrite Method for Cluster Analysis" was a classical work by Tadeusz Calinski and Joachim Harabasz, published in early Communications in Statistics (Calinski and Harabasz, 1974) Their method is now referred to as one of the most efficient techniques for determining the number of clusters (Milligan and Cooper, 1985; Everitt et al. 2001) At the end of 2010, it is actually second the most often cited work of Communications in Statistics and its two later parts, Theory Methods and Simulation Computation, right behind Kulldorff's 1997 article. Even though this work is so important, a sad thing is that in many articles it is incorrectly cited as: Calinski, R.B. Haratasz, J. (1974) A Dendrite Method for Cluster Analysis. Communications in Statistics 3(1):1 27. I have no idea where the initials R.B. come from I checked the original work, and it is "T. Calinski." (Of course, usually the Polish special character n is spelled as n, but this is not a problem and it is common; and this is not the point of this letter. It is probable that the mistake was made in one of early sources, and works which followed them and based solely on this source just repeated the mistake. For example, both references cited before Milligan and Cooper (1985) and Everitt et al. (2001) used the above incorrect citation. Also, a recent work in Communications in Statistics: Theory Methods (Garcia Escudero et al. 2009) used this citation. But the truth is that the incorrect citation is widespread, not only in scientific literature. For example, in R (R Development Core Team, 2010) thanks to the sos package (Garcia Escudero et al. 2009) I found the reference to the discussed work is made in the five following packages: FD (Laliberte and Legendre, 2010; Laliberte and Shipley, 2010) vegan (Oksanen et al. 2010) clues (Chang et al. 2010) clusterSim (Walesiak and Dudek, 2010) and fpc (Hennig, 2010) Only in the first two (FD and vegan) the citation was correct.

title: High content imaging analysis for detecting the loss of drebrin clusters along dendrites in cultured hippocampal neurons.
abstract: Detection of drug effects on neuronal synapses is important for predicting their adverse effects. We have used drebrin as a marker to detect the synaptic changes in cultured neurons. High concentration of glutamate decreases the amount of drebrin in synapses. To increase the availability of this method for high throughput analysis, we applied the drebrin based evaluation of synapses to high content image analysis using microplates.  Three weeks old cultured neurons were fixed and processed for immunocytochemistry to visualize drebrin clusters, dendrites and neuronal cell bodies. After automated image acquisition, total number of drebrin clusters per fields, linear density of drebrin cluster along dendrites, dendrite length and neuron number were automatically measured by a custom designed protocol.  Automated image acquisition and analysis showed that dendrite length and drebrin cluster density along dendrites are measured consistently and reproducibly. In addition, application of 10 100 mM glutamate for 10 min or 0.5 50 mM latrunculin A for 5 min significantly decreased drebrin cluster density without affecting neuron number. These results were consistent with our previous results using manual image acquisition and analysis with regular fluorescence microscope and image analysis software. Furthermore, 0.3 or 1.0 mM staurosporine for 24 h significantly decreased neuron number.  The present study demonstrates that this high throughput imaging analysis of drebrin cluster density along dendrites for detecting the effects of substances on synapses is sensitive enough to detect the effects of glutamate receptor activation and latrunculin A treatment, and indicates that this analysis will be useful for safety pharmacology study.

title: Effects of Secondary Dendrite Arm Spacing on Microporosity of A357 Alloy
abstract: Microporosity in cast aluminum alloys formed during solidification of castings can be due to the evolution of dissolved hydrogen gas from the liquid metal, the inability of liquid metal to feed through interdendritic channels, or a combination of both. The secondary dendrite arm spacing (SDAS) of the casting may affect either the evolution of hydrogen gas or the ability of feeding. In this investigation, a method based on the Nearest Neighbor Distance (NND for short) cluster analysis of image analysis data was used for distinguishing the gas and shrinkage pores in A357 alloy, in order to study the effects of SDAS on the microporosity formation. It shows that the shrinkage pores are prone to form in the specimens with small SDAS. The discrete, isolated gas microporosity is prone to form in the specimens with large SDAS.

title: A model for achieving the allocative efficiency of credit resources in Ukraine's banking system
abstract: The article presents a model for achieving the allocative efficiency of credit resources in Ukraine's banking system. The research involves establishing a set of criteria for assessing a borrower's creditworthiness and analyzing them by means of the discriminant analysis, Helwig's methods, cluster analysis, the dendrite method, and principal component analysis; the methods are, then, contrasted. This is followed by designing an optimal credit portfolio of the banking system and comparing it with actual credit portfolios with the help of similarity metrics.

title: Metallic Foam Structures, Dendrites and Implementation Optimizations for Phase Field Modeling
abstract: We present our current work in the field of computational materials science with the phase field method on the high performance cluster XC 4000 of the KIT (Karlsruhe Institute of Technology) Our investigations include heat conduction of open cell metal foams, dendritic growth and optimizations of the concurrent processing with the message passing interface (MPI) standard. Large scale simulations are applied to identify relevant parameters of heat conduction and dendrite growth. Our overall goal is to continuously develop our models, numerical solution techniques and software implementations. The basic model and parallelization scheme is described. Disadvantages of 1D domain decomposition compared to 3D domain decomposition for large 3D simulation domains are explained and a detailed analysis of the new 3D decomposition needs to be performed. The data throughput of parallel file IO operations is measured and system specific differences have been found which need further investigations.

title: Investigating injury severities of motorcycle riders: A two step method integrating latent class cluster analysis and random parameters logit model.
abstract: Due to the wide existence of heterogeneous nature in traffic safety data, traditional methods used to investigate motorcyclist rider injury severity always lead to masking of some underlying relationships which may be critical for the formulation of efficient safety countermeasures. Instead of applying one single model to the whole dataset or focusing on pre defined crash types as done in previous studies, the present study proposes a two step method integrating latent class cluster analysis and random parameters logit model to explore contributing factors influencing the injury levels of motorcyclists. A latent class cluster approach is first used to segment the motorcycle crashes into relatively homogeneous clusters. A mixed logit model is then elaborately developed for each cluster to identify its unique influential factors. The analysis was based on the police reported crash dataset (2015 2017) of Hunan province, China. The goodness of fit indicators and the Receiver Operating Characteristic curves show that the proposed method is more accurate when modeling the riders' injury severities. The heterogeneity found in each homogeneous subgroup supports the application of the random parameters logit model in the study. More importantly, the results demonstrate that segmenting motorcycle crashes into relatively homogeneous clusters as a preliminary step helps to uncover some important influencing factors hidden in the whole data model. The proposed method is proved to have great potential for accounting for the source of heterogeneity. The injury risk factors identified in specific cases provide more reliable information for traffic engineers and policymakers to improve motorcycle traffic safety.

title: A Bayesian cluster analysis method for single molecule localization microscopy data
abstract: Cell function is regulated by the spatiotemporal organization of the signaling machinery, and a key facet of this is molecular clustering. Here, we present a protocol for the analysis of clustering in data generated by 2D single molecule localization microscopy (SMLM) for example, photoactivated localization microscopy (PALM) or stochastic optical reconstruction microscopy (STORM) Three features of such data can cause standard cluster analysis approaches to be ineffective: (i) the data take the form of a list of points rather than a pixel array; (ii) there is a non negligible unclustered background density of points that must be accounted for; and (iii) each localization has an associated uncertainty in regard to its position. These issues are overcome using a Bayesian, model based approach. Many possible cluster configurations are proposed and scored against a generative model, which assumes Gaussian clusters overlaid on a completely spatially random (CSR) background, before every point is scrambled by its localization precision. We present the process of generating simulated and experimental data that are suitable to our algorithm, the analysis itself, and the extraction and interpretation of key cluster descriptors such as the number of clusters, cluster radii and the number of localizations per cluster. Variations in these descriptors can be interpreted as arising from changes in the organization of the cellular nanoarchitecture. The protocol requires no specific programming ability, and the processing time for one data set, typically containing 30 regions of interest, is ~18 h; user input takes ~1 h.

title: A METHOD FOR CLUSTER ANALYSIS.
abstract: A method for investigating the relation of points in multidimensional space is described. Using an analysis of variance technique, the points are divided into the two most compact clusters, and the pTocess repeated sequentially so that a tree diagram is formed. It is pointed out that the method is well suited to electronic computing. The application of the method to problems of classification is stressed, and numerical examples are given of applications in the analysis of chromosome patterns in cells.

title: A non parametric and scale independent method for cluster analysis
abstract: A non parametric and scale independent method for cluster analysis II: the multivariate case Received accepted 2  A general method is described for detecting and analysing galaxy systems. The multivariate geometrical structure of the sample is studied by using an extension of the method which we introduced in a previous paper. The method is based on an estimate of the probability density underlying a data sample. The density is estimated by using an iterative and adaptive kernel estimator. The used kernels have spherical symmetry, however we describe a method in order to estimate the locally optimal shape of the kernels. We use the results of the geometrical structure analysis in order to study the effects that is has on the cluster parameter estimate. This suggests a possible way to distinguish between structure and substructure within a sample. The method is tested by using simulated numerical models and applied to two galaxy samples taken from the literature. The results obtained for the Coma cluster suggest a core halo structure formed by a large number of geometrically independent systems. A different conclusion is suggested by the results for the Cancer cluster indicating the presence of at least two independent structures both containing substructure. The dynamical consequences of the results obtained from the geometrical analysis will be described in a later paper. Further applications of the method are suggested and are currently in progress.