Variable alignment of high resolution data by cluster analysis

Zheng, Peng; Harrington, Peter de B.; Craig, A; Fleming, Ronald H.

doi:10.1016/0003-2670(95)00149-t

Cited by 4 publications

(2 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Data characterization can be planned as a two-step procedure consisting of the combination of PCA for reduction of data dimensions followed by Cluster Analysis for grouping similar types of data objects. This technique has been widely used in several different types of applications in a diverse range of scientific fields including in crime analysis [ 24 ], in finding the relationship between retention parameters and physiochemical parameters of barbiturates [ 25 ], in chemo-metric methods in characterizing steel alloy samples [ 26 ], in drug design [ 27 ], in isolating single unit activities for data acquisition [ 28 ], and in microarray based gene identification [ 29 , 30 ]. This combined technique has been reviewed by [ 31 ] for several clustering algorithms, and they have emphasized the importance of applying PCA prior to Cluster Analysis for high dimensional data.…”

Section: Introductionmentioning

confidence: 99%

The discrimination of interaural level difference sensitivity functions: development of a taxonomic data template for modelling

Uragun

Rajan

2013

BMC Neurosci

View full text Add to dashboard Cite

BackgroundA major cue for the position of a high-frequency sound source in azimuth is the difference in sound pressure levels in the two ears, Interaural Level Differences (ILDs), as a sound is presented from different positions around the head. This study aims to use data classification techniques to build a descriptive model of electro-physiologically determined neuronal sensitivity functions for ILDs. The ILDs were recorded from neurons in the central nucleus of the Inferior Colliculus (ICc), an obligatory midbrain auditory relay nucleus. The majority of ICc neurons (~ 85%) show sensitivity to ILDs but with a variety of different forms that are often difficult to unambiguously separate into different information-bearing types. Thus, this division is often based on laboratory-specific and relatively subjective criteria. Given the subjectivity and non-uniformity of ILD classification methods in use, we examined if objective data classification techniques for this purpose. Our key objectives were to determine if we could find an analytical method (A) to validate the presence of four typical ILD sensitivity functions as is commonly assumed in the field, and (B) whether this method produced classifications that mapped on to the physiologically observed results.MethodsThe three-step data classification procedure forms the basic methodology of this manuscript. In this three-step procedure, several data normalization techniques were first tested to select a suitable normalization technique to our data. This was then followed by PCA to reduce data dimensionality without losing the core characteristics of the data. Finally Cluster Analysis technique was applied to determine the number of clustered data with the aid of the CCC and Inconsistency Coefficient values.ResultsThe outcome of a three-step analytical data classification process was the identification of seven distinctive forms of ILD functions. These seven ILD function classes were found to map to the four “known” ideal ILD sensitivity function types, namely: Sigmoidal-EI, Sigmoidal-IE, Peaked, and Insensitive, ILD functions, and variations within these classes. This indicates that these seven templates can be utilized in future modelling studies.ConclusionsWe developed a taxonomy of ILD sensitivity functions using a methodological data classification approach. The number and types of generic ILD function patterns found with this method mapped well on to our electrophysiologically determined ILD sensitivity functions. While a larger data set of the latter functions may bring a more robust outcome, this good mapping is encouraging in providing a principled method for classifying such data sets, and could be well extended to other such neuronal sensitivity functions, such as contrast tuning in vision.

show abstract

Section: Introductionmentioning

confidence: 99%

The discrimination of interaural level difference sensitivity functions: development of a taxonomic data template for modelling

Uragun

Rajan

2013

BMC Neurosci

View full text Add to dashboard Cite

show abstract

“…Classification of areas in SIMS images from surface analysis allowed the automatic detection of inorganic phases [13,14]. Cluster analysis has been applied for a variable alignment of high-resolution TOF-SIMS data from steel samples [15].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of mass spectra from organic compounds assumed to be present in cometary grains. Exploratory data analysis

Werther¹,

Demuth²,

Krueger³

et al. 2002

Journal of Chemometrics

View full text Add to dashboard Cite

From about 60 organic compounds, mass spectra have been measured by 252 Cf ionization mass spectrometry (PDMS). The substances and the data are relevant for future experiments near a comet by the space research project COSIMA (a time-of-flight mass spectrometer for secondary ions) on board of the ESA mission ROSETTA. The spectra have been transformed into a set of appropriate spectral features (x-variables); the corresponding chemical structures have been characterized by binary substructure descriptors (y-variables). PLS-2 mappings exhibit good correlations between the x-and the y-components. PLS spectrum±substructure mappings supported the detection of structural classes that are well reflected by spectral data. PCA with spectral data was less efficient for this purpose. The found structural classes are candidates for the development of spectral classifiers for the COSIMA project. Interpretation of the loading plots indicated spectral properties which are characteristic for substance classes.

show abstract

Artificial Intelligence and Expert Systems in Mass Spectrometry

Beavis

Colby

Goodacre

et al. 2000

Encyclopedia of Analytical Chemistry

View full text Add to dashboard Cite

This article provides a brief introduction to aspects of mass spectrometry (MS) that employ artificial intelligence (AI) and expert system (ES) technology. These areas have grown rapidly with the development of computer software and hardware capabilities. In many cases, they have become fundamental parts of modern mass spectrometers. Specific attention is paid to applications that demonstrate how important features of MS are now dependent on AI and ESs. The following topics are specifically covered: history, MS data systems, biological applications, artificial neural networks (ANNs), and optimization techniques.

show abstract

Variable alignment of high resolution data by cluster analysis

Cited by 4 publications

References 2 publications

The discrimination of interaural level difference sensitivity functions: development of a taxonomic data template for modelling

The discrimination of interaural level difference sensitivity functions: development of a taxonomic data template for modelling

Evaluation of mass spectra from organic compounds assumed to be present in cometary grains. Exploratory data analysis

Artificial Intelligence and Expert Systems in Mass Spectrometry

Contact Info

Product

Resources

About