Physical Sciences and Mathematics Commons^™

Data Mining Of Pancreatic Cancer Protein Databases, Peter Revesz, Christopher Assi

CSE Conference and Workshop Papers

Data mining of protein databases poses special challenges because many protein databases are non- relational whereas most data mining and machine learning algorithms assume the input data to be a type of rela- tional database that is also representable as an ARFF file. We developed a method to restructure protein databases so that they become amenable for various data mining and machine learning tools. Our restructuring method en- abled us to apply both decision tree and support vector machine classifiers to a pancreatic protein database. The SVM classifier that used both GO term and PFAM families to characterize proteins gave …

Adaptive Grid Based Localized Learning For Multidimensional Data, Sheetal Saini

Doctoral Dissertations

Rapid advances in data-rich domains of science, technology, and business has amplified the computational challenges of "Big Data" synthesis necessary to slow the widening gap between the rate at which the data is being collected and analyzed for knowledge. This has led to the renewed need for efficient and accurate algorithms, framework, and algorithmic mechanisms essential for knowledge discovery, especially in the domains of clustering, classification, dimensionality reduction, feature ranking, and feature selection. However, data mining algorithms are frequently challenged by the sparseness due to the high dimensionality of the datasets in such domains which is particularly detrimental to the …

Building A Computer Program To Support Children, Parents, And Distraction During Healthcare Procedures, Kirsten Hanrahan, Ann Marie Mccarthy, Charmaine Kleiber, Kaan Ataman, W. Nick Street, M. Bridget Zimmerman, Annel L. Ersig

Business Faculty Articles and Research

This secondary data analysis used data mining methods to develop predictive models of child risk for distress during a healthcare procedure. Data used came from a study that predicted factors associated with children's responses to an intravenous catheter insertion while parents provided distraction coaching. From the 255 items used in the primary study, 44 predictive items were identified through automatic feature selection and used to build support vector machine regression models. Models were validated using multiple cross-validation tests and by comparing variables identified as explanatory in the traditional versus support vector machine regression. Rule-based approaches were applied to the model …

Data Mining Of Protein Databases, Christopher Assi

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Data mining of protein databases poses special challenges because many protein databases are non-relational whereas most data mining and machine learning algorithms assume the input data to be a relational database. Protein databases are non-relational mainly because they often contain set data types. We developed new data mining algorithms that can restructure non-relational protein databases so that they become relational and amenable for various data mining and machine learning tools. We applied the new restructuring algorithms to a pancreatic protein database. After the restructuring, we also applied two classification methods, such as decision tree and SVM classifiers and compared their …

Ensemble Of Feature Selection Techniques For High Dimensional Data, Sri Harsha Vege

Masters Theses & Specialist Projects

Data mining involves the use of data analysis tools to discover previously unknown, valid patterns and relationships from large amounts of data stored in databases, data warehouses, or other information repositories. Feature selection is an important preprocessing step of data mining that helps increase the predictive performance of a model. The main aim of feature selection is to choose a subset of features with high predictive information and eliminate irrelevant features with little or no predictive information. Using a single feature selection technique may generate local optima.

In this thesis we propose an ensemble approach for feature selection, where multiple …