111 research outputs found
TSP--Infrastructure for the Traveling Salesperson Problem
No full textThe traveling salesperson (or, salesman) problem (TSP) is a well known and important combinatorial optimization problem. The goal is to find the shortest tour that visits each city in a given list exactly once and then returns to the starting city. Despite this simple problem statement, solving the TSP is difficult since it belongs to the class of NP-complete problems. The importance of the TSP arises besides from its theoretical appeal from the variety of its applications. Typical applications in operations research include vehicle routing, computer wiring, cutting wallpaper and job sequencing. The main application in statistics is combinatorial data analysis, e.g., reordering rows and columns of data matrices or identifying clusters. In this paper, we introduce the R package TSP which provides a basic infrastructure for handling and solving the traveling salesperson problem. The package features S3 classes for specifying a TSP and its (possibly optimal) solution as well as several heuristics to find good solutions. In addition, it provides an interface to Concorde, one of the best exact TSP solvers currently available.
TSP - Infrastructure for the Traveling Salesperson Problem
No full textThe traveling salesperson or salesman problem (TSP) is a well known and important combinatorial optimization problem. The goal is to find the shortest tour that visits each city in a given list exactly once and then returns to the starting city. Despite this simple problem statement, solving the TSP is difficult since it belongs to the class of NP-complete problems. The importance of the TSP arises besides from its theoretical appeal from the variety of its applications. In addition to vehicle routing, many other applications, e.g., computer wiring, cutting wallpaper, job sequencing or several data visualization techniques, require the solution of a TSP. In this paper we introduce the R package TSP which provides a basic infrastructure for handling and solving the traveling salesperson problem. The package features S3 classes for specifying a TSP and its (possibly optimal) solution as well as several heuristics to find good solutions. In addition, it provides an interface to Concorde, one of the best exact TSP solvers currently available. (author's abstract)Series: Research Report Series / Department of Statistics and Mathematic
Data for Quantitative Classification of Vortical Flows Based on Topological Features using Graph Matching
No full textThis resource contains the raw data (velocity vector fields and critical point data) used on the article Quantitative Classification of Vortical Flows Based on Topological Features using Graph Matching by Paul S. Krueger, Michael Hahsler, Eli V. Olinick, Sheila H. Williams, and Mohammadreza Zharfa. The format of the data is described in the file DataFormat.txt
Getting Things in Order: An Introduction to the R package seriation
No full textSeriation, i.e., finding a linear order for a set of objects given data and a loss or merit function, is a basic problem in data analysis. Caused by the problem's combinatorial nature, it is hard to solve for all but very small sets. Nevertheless, both exact solution methods and heuristics are available. In this paper we present the package seriation which provides the infrastructure for seriation with R. The infrastructure comprises data structures to represent linear orders as permutation vectors, a wide array of seriation methods using a consistent interface, a method to calculate the value of various loss and merit functions, and several visualization techniques which build on seriation. To illustrate how easily the package can be applied for a variety of applications, a comprehensive collection of examples is presented.Series: Research Report Series / Department of Statistics and Mathematic
Getting Things in Order: An Introduction to the R Package seriation
No full textSeriation, i.e., finding a suitable linear order for a set of objects given data and a loss or merit function, is a basic problem in data analysis. Caused by the problem's combinatorial nature, it is hard to solve for all but very small sets. Nevertheless, both exact solution methods and heuristics are available. In this paper we present the package seriation which provides an infrastructure for seriation with R. The infrastructure comprises data structures to represent linear orders as permutation vectors, a wide array of seriation methods using a consistent interface, a method to calculate the value of various loss and merit functions, and several visualization techniques which build on seriation. To illustrate how easily the package can be applied for a variety of applications, a comprehensive collection of examples is presented.
arules - A Computational Environment for Mining Association Rules and Frequent Item Sets
No full textMining frequent itemsets and association rules is a popular and well researched approach
for discovering interesting relationships between variables in large databases. The
R package arules presented in this paper provides a basic infrastructure for creating and
manipulating input data sets and for analyzing the resulting itemsets and rules. The package
also includes interfaces to two fast mining algorithms, the popular C implementations
of Apriori and Eclat by Christian Borgelt. These algorithms can be used to mine frequent
itemsets, maximal frequent itemsets, closed frequent itemsets and association rules. (authors' abstract
rEMM: Extensible Markov Model for Data Stream Clustering in R
No full textClustering streams of continuously arriving data has become an important application of data mining in recent years and efficient algorithms have been proposed by several researchers. However, clustering alone neglects the fact that data in a data stream is not only characterized by the proximity of data points which is used by clustering, but also by a temporal component. The extensible Markov model (EMM) adds the temporal component to data stream clustering by superimposing a dynamically adapting Markov chain. In this paper we introduce the implementation of the R extension package rEMM which implements EMM and we discuss some examples and applications.
arules - A Computational Environment for Mining Association Rules and Frequent Item Sets
No full textMining frequent itemsets and association rules is a popular and well researched approach for discovering interesting relationships between variables in large databases. The R package arules presented in this paper provides a basic infrastructure for creating and manipulating input data sets and for analyzing the resulting itemsets and rules. The package also includes interfaces to two fast mining algorithms, the popular C implementations of Apriori and Eclat by Christian Borgelt. These algorithms can be used to mine frequent itemsets, maximal frequent itemsets, closed frequent itemsets and association rules.
Developing and testing top-n recommendation algorithms for 0-1 data using recommenderlab
No full textThe problem of creating recommendations given a large data base from directly elicited ratings (e.g., ratings of 1 through 5 stars) is a popular research area which was lately boosted by the Netflix Prize competition. While computing recommendations using these type of data has direct application for example for large on-line retailers, there are many potential applications for recommender systems where such data is not available. However, in many cases there might be 0-1 rating data available or can be derived from other data sources (e.g., purchase records, Web click data) which can be utilized. Although this type of data differs significantly from directly elicited ratings, only very limited research is available for 0-1 data. This paper describes recommenderlab which provides the infrastructure to test and develop recommender algorithms. Currently the focus is on recommender systems for 0-1 data, however in the future it can be extended to also the more thoroughly researched case of directly elicited, real-valued rating data.
Optimizing Web Sites for Customer Retention*
No full textWith customer relationship management (CRM) companies move away from a mainly product-centered view to a customer-centered view. Resulting from this change, the effective management of how to keep contact with customers throughout different channels is one of the key success factors in today’s business world. Company Web sites have evolved in many industries into an extremely important channel through which customers can be attracted and retained. To analyze and optimize this channel, accurate models of how customers browse through the Web site and what information within the site they repeatedly view are crucial. Typically, data mining techniques are used for this purpose. However, there already exist numerous models developed in marketing research for traditional channels which could also prove valuable to understanding this new channel. In this paper we propose the application of an extension of the Logarithmic Series Distribution (LSD) model repeat-usage of Web-based information and thus to analyze and optimize a Web Site’s capability to support one goal of CRM, to retain customers. As an example, we use the university’s blended learning web portal with over a thousand learning resources to demonstrate how the model can be used to evaluate and improve the Web site’s effectiveness. 1
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