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Articles 1 - 8 of 8
Full-Text Articles in Physical Sciences and Mathematics
Data Science Applied To Discover Ancient Minoan-Indus Valley Trade Routes Implied By Commonweight Measures, Peter Revesz
Data Science Applied To Discover Ancient Minoan-Indus Valley Trade Routes Implied By Commonweight Measures, Peter Revesz
CSE Conference and Workshop Papers
This paper applies data mining of weight measures to discover possible long-distance trade routes among Bronze Age civilizations from the Mediterranean area to India. As a result, a new northern route via the Black Sea is discovered between the Minoan and the Indus Valley civilizations. This discovery enhances the growing set of evidence for a strong and vibrant connection among Bronze Age civilizations.
A Review Of Drought Monitoring Using Remote Sensing And Data Mining Methods, R. Inoubli, A.B. Abbes, I.R. Farah, V. Singh, T. Tadesse, A.Z. Abiy
A Review Of Drought Monitoring Using Remote Sensing And Data Mining Methods, R. Inoubli, A.B. Abbes, I.R. Farah, V. Singh, T. Tadesse, A.Z. Abiy
School of Natural Resources: Faculty Publications
No abstract provided.
So What Are You Going To Do With That? The Promises And Pitfalls Of Massive Data Sets, Sigrid Anderson Cordell, Melissa Gomis
So What Are You Going To Do With That? The Promises And Pitfalls Of Massive Data Sets, Sigrid Anderson Cordell, Melissa Gomis
UNL Libraries: Faculty Publications
This article takes as its case study the challenge of data sets for text mining, sources that offer tremendous promise for digital humanities (DH) methodology but present specific challenges for humanities scholars. These text sets raise a range of issues: What skills do you train humanists to have? What is the library’s role in enabling and supporting use of those materials? How do you allocate staff? Who oversees sustainability and data management? By addressing these questions through a specific use case scenario, this article shows how these questions are central to mapping out future directions for a range of library …
Data Mining The Functional Characterizations Of Proteins To Predict Their Cancer-Relatedness, Peter Revesz, Christopher Assi
Data Mining The Functional Characterizations Of Proteins To Predict Their Cancer-Relatedness, Peter Revesz, Christopher Assi
School of Computing: Faculty Publications
This paper considers two types of protein data. First, data about protein function described in a number of ways, such as, GO terms and PFAM families. Second, data about whether individual proteins are experimentally associated with cancer by an anomalous elevation or lowering of their expressions within cancerous cells. We combine these two types of protein data and test whether the first type of data, that is, the functional descriptors, can predict the second type of data, that is, cancer-relatedness. By using data mining and machine learning, we derive a classifier algorithm that using only GO term and PFAM family …
Data Mining Of Pancreatic Cancer Protein Databases, Peter Revesz, Christopher Assi
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 …
Data Mining Of Protein Databases, Christopher Assi
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 …
Redistricting Using Constrained Polygonal Clustering, Deepti Joshi, Leen-Kiat Soh, Ashok Samal
Redistricting Using Constrained Polygonal Clustering, Deepti Joshi, Leen-Kiat Soh, Ashok Samal
School of Computing: Faculty Publications
Redistricting is the process of dividing a geographic area consisting of spatial units—often represented as spatial polygons—into smaller districts that satisfy some properties. It can therefore be formulated as a set partitioning problem where the objective is to cluster the set of spatial polygons into groups such that a value function is maximized [1]. Widely used algorithms developed for point-based data sets are not readily applicable because polygons introduce the concepts of spatial contiguity and other topological properties that cannot be captured by representing polygons as points. Furthermore, when clustering polygons, constraints such as spatial contiguity and unit distributedness should …
Drought Monitoring Using Data Mining Techniques: A Case Study For Nebraska, Usa, Tsegaye Tadesse, Donald A. Wilhite, Sherri K. Harms, Michael J. Hayes, Steve Goddard
Drought Monitoring Using Data Mining Techniques: A Case Study For Nebraska, Usa, Tsegaye Tadesse, Donald A. Wilhite, Sherri K. Harms, Michael J. Hayes, Steve Goddard
Drought Mitigation Center: Faculty Publications
Drought has an impact on many aspects of society. To help decision makers reduce the impacts of drought, it is important to improve our understanding of the characteristics and relationships of atmospheric and oceanic parameters that cause drought. In this study, the use of data mining techniques is introduced to find associations between drought and several oceanic and climatic indices that could help users in making knowledgeable decisions about drought responses before the drought actually occurs. Data mining techniques enable users to search for hidden patterns and find association rules for target data sets such as drought episodes. These techniques …