Machine Learning Clustering Techniques Research Articles

Reactivity of Amorphous Carbon Surfaces: Rationalizing the Role of Structural Motifs in Functionalization Using Machine Learning.

Systematic atomistic studies of surface reactivity for amorphous materials have not been possible in the past because of the complexity of these materials and the lack of the computer power necessary to draw representative statistics. With the emergence and popularization of machine learning (ML) approaches in materials science, systematic (and accurate) studies of the surface chemistry of disordered materials are now coming within reach. In this paper, we show how the reactivity of amorphous carbon (a-C) surfaces can be systematically quantified and understood by a combination of ML interatomic potentials, ML clustering techniques, and density functional theory calculations. This methodology allows us to process large amounts of atomic data to classify carbon atomic motifs on the basis of their geometry and quantify their reactivity toward hydrogen- and oxygen-containing functionalities. For instance, we identify subdivisions of sp and sp2 motifs with markedly different reactivities. We therefore draw a comprehensive, both qualitative and quantitative, picture of the surface chemistry of a-C and its reactivity toward −H, −O, −OH, and −COOH. While this paper focuses on a-C surfaces, the presented methodology opens up a new systematic and general way to study the surface chemistry of amorphous and disordered materials.

Measuring the Feasibility of Clustering Techniques on Usability Performance Data

This paper proposes a methodology that utilizes unsupervised machine learning clustering techniques in performance based usability data. This paper will first discuss the introduction and current works in the aforementioned domain followed by proposing the methodology to compare and find a better clustering algorithm in processing usability performance data in the field of mobile augmented reality interfaces. The paper will then present the results yield from an experiment abiding by the proposed methodology and discusses the analysis. The paper will end with a short discussion followed by proposed future works in the research area. Keywords: Augmented Reality, Clustering, Machine Learning, Mobile, Usability

The Analysis of Anticancer Drug Sensitivity of Lung Cancer Cell Lines by using Machine Learning Clustering Techniques

Lung cancer is the commonest type of cancer with the highest fatality rate worldwide. There is continued research that experiments on drug development for lung cancer patients by assessing their responses to chemotherapeutic treatments to select novel targets for improved therapies. This study aims to analyze the anticancer drug sensitivity in human lung cancer cell lines by using machine learning techniques. The data for this analysis is extracted from the National Cancer Institute (NCI). This experiment uses 408,291 human small molecule lung cancer cell lines to conclude. The values are drawn from describing the raw viability values for 91 human lung cancer cell lines treated with 354 different chemical compounds and 432 concentration points tested in each replicate experiments. Our analysis demonstrated the data from a considerable amount of cell lines clustered by using Simple K-means, Filtered clustering and by calculating sensitive drugs for each lung cancer cell line. Additionally, our analysis also demonstrated that the Neopeltolide, Parbendazole, Phloretin and Piperlongumine anti-drug chemical compounds were more sensitive for all 91 cell lines under different concentrations (p-value < 0.001). Our findings indicated that Simple K-means and Filtered clustering methods are completely similar to each other. The available literature on lung cancer cell line data observed a significant relationship between lung cancer and anticancer drugs. Our analysis of the reported experimental results demonstrated that some compounds are more sensitive than other compounds; Phloretin was the most sensitive compound for all lung cancer cell lines which were nearly about 59% out of 91 cell lines. Hence, our observation provides the methodology on how anticancer drug sensitivity of lung cancer cell lines can be analyzed by using machine learning techniques, such as clustering algorithms. This inquiry is a useful reference for researchers who are experimenting on drug developments for the lung cancer in the future.

Automatic characterization of ignition processes with machine learning clustering techniques

AbstractMachine learning clustering techniques are used to characterize and, after the training phase, to identify phases within an ignition process. For the ethanol mechanism used in this paper, four physically identifiable phases were found and characterized: the initiation phase, preignition phase, ignition phase, and the postignition phase. The clustering is done with respect to fuzzy logic predicates identifying the maxima, minima, and inflection points of the species profiles. The cluster descriptions characterize the phases found and are in human interpretable form. In addition, these descriptions are powerful enough to be used to predict the phase structure under new conditions. Cluster phases were calculated for the ethanol mechanism at an equivalence ratio of 0.5, a pressure of 3.3 bar, and the temperatures 1200, 1300, 1400, and 1500 K. The resulting cluster phase descriptions were then successfully used to predict the phase structure and ignition delay times for other temperatures in the range from 1200 to 1500 K. The effect of different fuzzy logic predicate profile descriptions is studied to emphasize that the boundaries of some phases, specifically that between the preignition and the ignition phase, are a matter of what the modeler considers important. The end of the ignition phase corresponds to the ignition delay time and was relatively independent of the predicate descriptions used to determine the phases. © 2006 Wiley Periodicals, Inc. Int J Chem Kinet 38: 621–633, 2006

Characterizing complex reaction mechanisms using machine learning clustering techniques

AbstractA machine learning conceptual clustering method applied to reaction mechanisms provides an automatic and, hence, unbiased means to differentiate between reactive phases within a total reactive process. Similar reactive phases were defined by means of local reaction sensitivity values. The method was applied to the Hochgreb and Dryer aldehyde combustion mechanism of 36 reactions. Three major time ranges were found and characterized: an initial phase of aldehyde reaction, an intermediate phase where only a small amount of aldehyde is left, and an end phase of reactions to final products. Further refinements of these phases into subtime intervals were found. All ranges found could be chemically justified. This method is meant as a supplement to existing methods of mechanism analysis and its main purpose is the automatic characterization of existing mechanisms and can potentially be used for mechanism reduction. © 2003 Wiley Periodicals, Inc. Int J Chem Kinet 36: 107–118, 2004