Artificial intelligence · Data mining · Optimization

Research contributions from methods to applications

My publications span artificial intelligence, machine learning, data mining, optimization, and applied data science, with an emphasis on reproducible methods and open-source software.

Visualization

[1]	Michael Hahsler. arulesViz: Interactive visualization of association rules with R. R Journal, 9(2):163--175, December 2017. [ DOI | at the publisher ] Association rule mining is a popular data mining method to discover interesting relationships between variables in large databases. An extensive toolbox is available in the R-extension package arules. However, mining association rules often results in a vast number of found rules, leaving the analyst with the task to go through a large set of rules to identify interesting ones. Sifting manually through extensive sets of rules is time-consuming and strenuous. Visualization and especially interactive visualization has a long history of making large amounts of data better accessible. The R-extension package arulesViz provides most popular visualization techniques for association rules. In this paper, we discuss recently added interactive visualizations to explore association rules and demonstrate how easily they can be used in arulesViz via a unified interface. With examples, we help to guide the user in selecting appropriate visualizations and interpreting the results.
[2]	Michael Hahsler and Radoslaw Karpienko. Visualizing association rules in hierarchical groups. Journal of Business Economics, 87(3):317--335, May 2016. [ DOI | at the publisher ] Association rule mining is one of the most popular data mining methods. However, mining association rules often results in a very large number of found rules, leaving the analyst with the task to go through all the rules and discover interesting ones. Sifting manually through large sets of rules is time consuming and strenuous. Visualization has a long history of making large amounts of data better accessible using techniques like selecting and zooming. However, most association rule visualization techniques are still falling short when it comes to a large number of rules. In this paper we present an integrated framework for post-processing and visualization of association rules, which allows to intuitively explore and interpret highly complex scenarios. We demonstrate how this framework can be used to analyze large sets of association rules using the R software for statistical computing, and provide examples from the implementation in the R-package arulesViz.
[3]	Anurag Nagar and Michael Hahsler. Fast discovery and visualization of conserved regions in DNA sequences using quasi-alignment. BMC Bioinformatics, 14(Suppl. 11), 2013. [ DOI | at the publisher ] Next Generation Sequencing techniques are producing enormous amounts of biological sequence data and analysis becomes a major computational problem. Currently, most analysis, especially the identification of conserved regions, relies heavily on Multiple Sequence Alignment, which has a polynomial run time in terms of the number of sequences. Often significant computational resources are required. In this work, we present a method to efficiently discover regions of high similarity across multiple sequences without performing expensive sequence alignment. The method is based on approximating edit distance between segments of sequences using p-mer frequency counts. Then, efficient high-throughput data stream clustering is used to group highly similar segments into so called quasi-alignments. Quasi-alignments can be used for a variety of tasks such as species characterization and identification, phylogenetic analysis, functional analysis of sequences and, as in this paper, for discovering conserved regions. In this paper, we show that quasi-alignments can be used to discover highly similar segments across multiple sequences from related or different genomes efficiently and accurately. Experiments on a large number of unaligned 16S rRNA sequences obtained from the Greengenes database show that the method is able to identify conserved regions which agree with know hypervariable regions in 16S rRNA. Furthermore, the experiments show that the proposed method scales well for large data sets with a run time linear in the number of sequences, whereas existing multiple sequence alignment methods (such as Clustal) need a superlinear polynomial run time. Quasi-alignment-based algorithms can detect highly similar regions and conserved areas across multiple sequences. Since the run time is linear and the sequences are converted into a compact clustering model, we are able to identify conserved regions fast or even interactively using a standard PC. Our method has many potential applications such as finding characteristic signature sequences for families of organisms and studying conserved and variable regions in, for example, 16S rRNA.
[4]	Anurag Nagar and Michael Hahsler. A novel quasi-alignment-based method for discovering conserved regions in genetic sequences. In Proceedings of the IEEE BIBM 2012 Workshop on Data-Mining of Next-Generation Sequencing. IEEE Computer Society Press, October 2012. [ DOI | preprint (PDF) ] This paper presents an alignment-free technique to efficiently discover similar regions in large sets of biological sequences using position sensitive p-mer frequency clustering. A set of sequences is broken down into segment and then a frequency distribution over all oligomers of size p (referred to as p-mers) is obtained to summarize each segment. These summaries are clustered while the order of segments in the set of sequences is preserved in a Markov-type model. Sequence segments within each cluster have very similar DNA/RNA patterns and form a so called quasi-alignment. This fact can be used for a variety of tasks such as species characterization and identification, phylogenetic analysis, functional analysis of sequences and, as in this paper, for discovering conserved regions. Our method is computationally more efficient than multiple sequences alignment since it can apply modern data stream clustering algorithms which run in time linear in the number of segments and thus can help discover highly similar regions across a large number of sequences efficiently. In this paper, we apply the approach to efficiently discover and visualize conserved regions in 16S rRNA.
[5]	Michael Hahsler and Kurt Hornik. Dissimilarity plots: A visual exploration tool for partitional clustering. Journal of Computational and Graphical Statistics, 10(2):335--354, June 2011. [ DOI | preprint (PDF) ] For hierarchical clustering, dendrograms are a convenient and powerful visualization technique. Although many visualization methods have been suggested for partitional clustering, their usefulness deteriorates quickly with increasing dimensionality of the data and/or they fail to represent structure between and within clusters simultaneously. In this paper we extend (dissimilarity) matrix shading with several reordering steps based on seriation techniques. Both ideas, matrix shading and reordering, have been well-known for a long time. However, only recent algorithmic improvements allow us to solve or approximately solve the seriation problem efficiently for larger problems. Furthermore, seriation techniques are used in a novel stepwise process (within each cluster and between clusters) which leads to a visualization technique that is able to present the structure between clusters and the micro-structure within clusters in one concise plot. This not only allows us to judge cluster quality but also makes mis-specification of the number of clusters apparent. We give a detailed discussion of the construction of dissimilarity plots and demonstrate their usefulness with several examples. Experiments show that dissimilarity plots scale very well with increasing data dimensionality.
[6]	Michael Hahsler and Sudheer Chelluboina. Visualizing association rules in hierarchical groups. Unpublished. Presented at the 42nd Symposium on the Interface: Statistical, Machine Learning, and Visualization Algorithms (Interface 2011), June 2011. [ preprint (PDF) ] Association rule mining is one of the most popular data mining methods. However, mining association rules often results in a very large number of found rules, leaving the analyst with the task to go through all the rules and discover interesting ones. Sifting manually through large sets of rules is time consuming and strenuous. Visualization has a long history of making large amounts of data better accessible using techniques like selecting and zooming. However, most association rule visualization techniques are still falling short when it comes to a large number of rules. In this paper we present a new interactive visualization technique which lets the user navigate through a hierarchy of groups of association rules. We demonstrate how this new visualization techniques can be used to analyze a large sets of association rules with examples from our implementation in the R-package arulesViz.
[7]	Michael Hahsler, Sudheer Chelluboina, Kurt Hornik, and Christian Buchta. The arules R-package ecosystem: Analyzing interesting patterns from large transaction datasets. Journal of Machine Learning Research, 12:1977--1981, 2011. [ at the publisher ] This paper describes the ecosystem of R add-on packages developed around the infrastructure provided by the package arules. The packages provide comprehensive functionality for analyzing interesting patterns including frequent itemsets, association rules, frequent sequences and for building applications like associative classification. After discussing the ecosystem's design we illustrate the ease of mining and visualizing rules with a short example.
[8]	Michael Hahsler, Kurt Hornik, and Christian Buchta. Getting things in order: An introduction to the R package seriation. Journal of Statistical Software, 25(3):1--34, March 2008. [ DOI | at the publisher ] Seriation, 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.
[9]	Michael Hahsler and Kurt Hornik. TSP -- Infrastructure for the traveling salesperson problem. Journal of Statistical Software, 23(2):1--21, December 2007. [ DOI | at the publisher ] The 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.