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- All Subjects: Industry
- Creators: Andrew, Benjamin
Description
In this thesis, the application of pixel-based vertical axes used within parallel coordinate plots is explored in an attempt to improve how existing tools can explain complex multivariate interactions across temporal data. Several promising visualization techniques are combined, such as: visual boosting to allow for quicker consumption of large data sets, the bond energy algorithm to find finer patterns and anomalies through contrast, multi-dimensional scaling, flow lines, user guided clustering, and row-column ordering. User input is applied on precomputed data sets to provide for real time interaction. General applicability of the techniques are tested against industrial trade, social networking, financial, and sparse data sets of varying dimensionality.
ContributorsHayden, Thomas (Author) / Maciejewski, Ross (Thesis advisor) / Wang, Yalin (Committee member) / Runger, George C. (Committee member) / Mack, Elizabeth (Committee member) / Arizona State University (Publisher)
Created2014
Description
Understanding the necessary skills required to work in an industry is a difficult task with many potential uses. By being able to predict the industry of a person based on their skills, professional social networks could make searching better with automated tagging, advertisers can target more carefully, and students can better find a career path that fits their skillset. The aim in this project is to apply deep learning to the world of professional networking. Deep Learning is a type of machine learning that has recently been making breakthroughs in the analysis of complex datasets that previously were not of much use. Initially the goal was to apply deep learning to the skills-to-company relationship, but a lack of quality data required a change to the skills-to-industry relationship. To accomplish the new goal, a database of LinkedIn profiles that are part of various industries was gathered and processed. From this dataset a model was created to take a list of skills and output an industry that people with those skills work in. Such a model has value in the insights that it forms allowing candidates to: determine what industry fits a skillset, identify key skills for industries, and locate which industries possible candidates may best fit in. Various models were trained and tested on a skill to industry dataset. The model was able to learn similarities between industries, and predict the most likely industries for each profiles skillset.
ContributorsAndrew, Benjamin (Co-author) / Thiel, Alex (Co-author) / Sodemann, Angela (Thesis director) / Sebold, Brent (Committee member) / Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12