18 research outputs found

    Ranking of multidimensional drug profiling data by fractional-adjusted bi-partitional scores

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    Motivation: The recent development of high-throughput drug profiling (high content screening or HCS) provides a large amount of quantitative multidimensional data. Despite its potentials, it poses several challenges for academia and industry analysts alike. This is especially true for ranking the effectiveness of several drugs from many thousands of images directly. This paper introduces, for the first time, a new framework for automatically ordering the performance of drugs, called fractional adjusted bi-partitional score (FABS). This general strategy takes advantage of graph-based formulations and solutions and avoids many shortfalls of traditionally used methods in practice. We experimented with FABS framework by implementing it with a specific algorithm, a variant of normalized cut—normalized cut prime (FABS-NC′), producing a ranking of drugs. This algorithm is known to run in polynomial time and therefore can scale well in high-throughput applications

    Archives of Data Science, Series A. Vol. 1,1: Special Issue: Selected Papers of the 3rd German-Polish Symposium on Data Analysis and Applications

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    The first volume of Archives of Data Science, Series A is a special issue of a selection of contributions which have been originally presented at the {\em 3rd Bilateral German-Polish Symposium on Data Analysis and Its Applications} (GPSDAA 2013). All selected papers fit into the emerging field of data science consisting of the mathematical sciences (computer science, mathematics, operations research, and statistics) and an application domain (e.g. marketing, biology, economics, engineering)

    Multivariate Analysis in Management, Engineering and the Sciences

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    Recently statistical knowledge has become an important requirement and occupies a prominent position in the exercise of various professions. In the real world, the processes have a large volume of data and are naturally multivariate and as such, require a proper treatment. For these conditions it is difficult or practically impossible to use methods of univariate statistics. The wide application of multivariate techniques and the need to spread them more fully in the academic and the business justify the creation of this book. The objective is to demonstrate interdisciplinary applications to identify patterns, trends, association sand dependencies, in the areas of Management, Engineering and Sciences. The book is addressed to both practicing professionals and researchers in the field

    The Impact of Community Cohesion on Crime

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    Community cohesion generally acts to increase the safety of communities by increasing informal guardianship, and enhancing the work of formal crime prevention organisations. Understanding the dynamics of local social interactions is essential for community building. However, community cohesion is difficult to empirically quantify, because there are no obvious and direct indicators of community cohesion collected at population levels within official datasets. A potentially more promising alternative for estimating community cohesion is through the use of data from social media. Social media offers an opportunity for exploring networks of social interactions in a local community. This research will use social media data to explore the impact of community cohesion on crime. Sentiment analysis of tweets can help to uncover patterns of community mood in different areas. Modelling of community engagement on Facebook is useful for understanding patterns of social interactions and the strength of social networks in local communities. The central contribution of this thesis is the use of new metrics that estimate popularity, commitment and virality known as the PCV indicators for quantifying community cohesion on social media. These metrics, combined with diversity statistics constructed from “traditional” Census data, provide a better correlate of community cohesion and crime. To demonstrate the viability of this novel method for estimating the impact of community cohesion, a model of community engagement and burglary rates is constructed using Leeds community areas as an example. By examining the diversity of different community areas and strength of their social networks, from traditional and new data sources; it was found that stability and strong social media engagement in a local area are associated with lower burglary rates. The proposed new method can provide a better alternative for estimating community cohesion and its impact on crime. It is recommended that policy planning for resource allocation and community building needs to consider social structure and social networks in different communities

    Projection-Based Clustering through Self-Organization and Swarm Intelligence

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    It covers aspects of unsupervised machine learning used for knowledge discovery in data science and introduces a data-driven approach to cluster analysis, the Databionic swarm (DBS). DBS consists of the 3D landscape visualization and clustering of data. The 3D landscape enables 3D printing of high-dimensional data structures. The clustering and number of clusters or an absence of cluster structure are verified by the 3D landscape at a glance. DBS is the first swarm-based technique that shows emergent properties while exploiting concepts of swarm intelligence, self-organization and the Nash equilibrium concept from game theory. It results in the elimination of a global objective function and the setting of parameters. By downloading the R package DBS can be applied to data drawn from diverse research fields and used even by non-professionals in the field of data mining

    Projection-Based Clustering through Self-Organization and Swarm Intelligence: Combining Cluster Analysis with the Visualization of High-Dimensional Data

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    Cluster Analysis; Dimensionality Reduction; Swarm Intelligence; Visualization; Unsupervised Machine Learning; Data Science; Knowledge Discovery; 3D Printing; Self-Organization; Emergence; Game Theory; Advanced Analytics; High-Dimensional Data; Multivariate Data; Analysis of Structured Dat
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