DOCSLIB.ORG

Digging Deeper Into the Methods of Computational Chemistry By

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Digging Deeper Into the Methods of Computational Chemistry By Digging Deeper into the Methods of Computational Chemistry by Joshua A. Kammeraad A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Chemistry and Scientific Computing) in the University of Michigan 2020 Doctoral Committee: Associate Professor Paul M Zimmerman, Chair Professor Charles L. Brooks III Professor Robert Krasny Professor Roseanne Sension Joshua A. Kammeraad [email protected] ORCID iD: 0000-0003-0386-7198 © Joshua A. Kammeraad 2020 Dedication To God be all the glory. ii Acknowledgements First, I would like to thank my graduate mentor Paul Zimmerman for his mentorship. Paul learned my quirkiness and how to individually mentor me. His focus and direction kept me on track and his patience in challenging seasons is appreciated. I would also like to thank the rest of my graduate committee for their advice and feedback throughout this PhD program. I would like to acknowledge the rest of the Zimmerman lab, particularly Ian Pendleton, Alan Chien, and Andrew Molina for welcoming me into the lab and engaging in deep conversations of an academic and personal nature. I would like to thank Cody Aldaz for letting me be a part of his cool projects. Thank you Ambuj Tewari and everyone involved in our machine learning collaborations: Jack, Mina, Eric, Exequiel, Ziping, Tarun, and Eunjae. Ryan Hayes and Allison Roessler, thank you for all the times together in prayer for each other and our labs. I am also grateful for all of the mentors and teachers who prepared me for graduate school, particularly professors DeJongh, Pennings, Polik, and Cinzori for their support throughout my undergraduate education. Thank you to my cross country teammates including my roommates Tim Lewis and David Dolfin as well as Hania Szymczak, Michelle Kerr, and Jess Gaines for all of our nerdy, serious, and silly running conversations that kept me sane and the long nights and weekends of studying together that prepared me so well for this program. Thank you Dave Brzezinski, Jack Geddes, David Taylor, and Paul Webb from Grace Bible Church for your mentorship throughout my graduate studies. I am deeply grateful to my spiritual brothers and sisters in Impact Graduate Christian Fellowship without whom I could not have survived this program, including my accountability partners for their encouragement and support of my continued growth: Sinsar Hsie, Mark Dong, Calvin Montana, Matt Cui, Alex Wang, and Joseph Tu. A special thanks to each of the brothers and sisters I've had the privilege of serving closely with: Nancy Wu, Josh Cheng, Sara Timberlake, and Anita Luong. Our partnerships have sculpted and sharpened my character and your patience and support in my weakness is appreciated. Thank you Jasmine Jones for being so relentlessly selfless including spontaneously helping prepare me for my oral defense while I recovered from illness. iii James Tan and Matt Hughes, I have a deep appreciation for your loyal brotherhood in Christ and fighting with me through some of the hardest times of the past 5 years. To my family, thank you for being so loving and supportive of all stages of my education. Finally, I am eternally grateful to Jesus Christ my Savior and Lord for this beautiful, elegant universe and the capacity to explore it. iv Table of Contents Dedication ...................................................................................................................................... ii Acknowledgements ...................................................................................................................... iii List of Figures .............................................................................................................................. vii List of Schemes .............................................................................................................................. x List of Tables ................................................................................................................................ xi Abstract ........................................................................................................................................ xii Chapter 1. Introduction ........................................................................................................... 1 Chapter Overviews...................................................................................................................... 1 Chapter 2 ................................................................................................................................. 1 Chapter 3 ................................................................................................................................. 2 Chapter 4 ................................................................................................................................. 2 Chapter 5 ................................................................................................................................. 3 Themes ........................................................................................................................................ 3 Chapter 2. Estimating the Derivative Coupling Vector using Gradients ........................... 6 Main Text .................................................................................................................................... 6 Methods..................................................................................................................................... 15 Chapter 3. What Does the Machine Learn? Knowledge Representations of Chemical Reactivity ............................................................... 18 Introduction ............................................................................................................................... 18 A First Challenge: Representing Chemical Data ...................................................................... 22 Relationships Between Representations ................................................................................... 24 v Deconstruction of Machine Model-Making.............................................................................. 29 Reestablishing Chemical Concepts ........................................................................................... 30 Evans-Polanyi Relationships .................................................................................................... 33 Discussion ................................................................................................................................. 36 Conclusions ............................................................................................................................... 40 Computational Details .............................................................................................................. 41 Reaction Representations ...................................................................................................... 41 Dataset................................................................................................................................... 43 Machine Learning Pipeline ................................................................................................... 45 Appendix to Chapter 3 .............................................................................................................. 47 Note About Neural Network Topologies .............................................................................. 47 Note About Representing Atoms .......................................................................................... 52 Note on Data Postprocessing ................................................................................................ 62 Reactions Appearing in Data Set 1 ....................................................................................... 63 Chapter 4. Human – Algorithm Interactive Approach to Conformer Generation ......... 66 Introduction ............................................................................................................................... 66 Materials and Methods .............................................................................................................. 70 Results and Discussion ............................................................................................................. 75 Future Directions ...................................................................................................................... 79 Chapter 5. Final Thoughts .................................................................................................... 82 Open Questions and Research Opportunities ........................................................................... 83 How to Approach Future Challenges ........................................................................................ 84 Developing Multidisciplinary Scientific Leaders ................................................................. 85 Building a Strong Research Community with a Multidisciplinary Mindset ......................... 85 Active Engagement with Other Scientists ............................................................................ 87 Conclusion ................................................................................................................................ 87 Bibliography ...............................................................................................................................
Load more

Recommended publications
  • Semantic Web Integration of Cheminformatics Resources with the SADI Framework Leonid L Chepelev1* and Michel Dumontier1,2,3
    Chepelev and Dumontier Journal of Cheminformatics 2011, 3:16 http://www.jcheminf.com/content/3/1/16 METHODOLOGY Open Access Semantic Web integration of Cheminformatics resources with the SADI framework Leonid L Chepelev1* and Michel Dumontier1,2,3 Abstract Background: The diversity and the largely independent nature of chemical research efforts over the past half century are, most likely, the major contributors to the current poor state of chemical computational resource and database interoperability. While open software for chemical format interconversion and database entry cross-linking have partially addressed database interoperability, computational resource integration is hindered by the great diversity of software interfaces, languages, access methods, and platforms, among others. This has, in turn, translated into limited reproducibility of computational experiments and the need for application-specific computational workflow construction and semi-automated enactment by human experts, especially where emerging interdisciplinary fields, such as systems chemistry, are pursued. Fortunately, the advent of the Semantic Web, and the very recent introduction of RESTful Semantic Web Services (SWS) may present an opportunity to integrate all of the existing computational and database resources in chemistry into a machine-understandable, unified system that draws on the entirety of the Semantic Web. Results: We have created a prototype framework of Semantic Automated Discovery and Integration (SADI) framework SWS that exposes the QSAR descriptor functionality of the Chemistry Development Kit. Since each of these services has formal ontology-defined input and output classes, and each service consumes and produces RDF graphs, clients can automatically reason about the services and available reference information necessary to complete a given overall computational task specified through a simple SPARQL query.
    [Show full text]
  • Joelib Tutorial
    JOELib Tutorial A Java based cheminformatics/computational chemistry package Dipl. Chem. Jörg K. Wegner JOELib Tutorial: A Java based cheminformatics/computational chemistry package by Dipl. Chem. Jörg K. Wegner Published $Date: 2004/03/16 09:16:14 $ Copyright © 2002, 2003, 2004 Dept. Computer Architecture, University of Tübingen, GermanyJörg K. Wegner Updated $Date: 2004/03/16 09:16:14 $ License This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation version 2 of the License. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. Documents PS (JOELibTutorial.ps), PDF (JOELibTutorial.pdf), RTF (JOELibTutorial.rtf) versions of this tutorial are available. Plucker E-Book (http://www.plkr.org) versions: HiRes-color (JOELib-HiRes-color.pdb), HiRes-grayscale (JOELib-HiRes-grayscale.pdb) (recommended), HiRes-black/white (JOELib-HiRes-bw.pdb), color (JOELib-color.pdb), grayscale (JOELib-grayscale.pdb), black/white (JOELib-bw.pdb) Revision History Revision $Revision: 1.5 $ $Date: 2004/03/16 09:16:14 $ $Id: JOELibTutorial.sgml,v 1.5 2004/03/16 09:16:14 wegner Exp $ Table of Contents Preface ........................................................................................................................................................i 1. Installing JOELib
    [Show full text]
  • Cheminformatics and Chemical Information
    Cheminformatics and Chemical Information Matt Sundling Advisor: Prof. Curt Breneman Department of Chemistry and Chemical Biology/Center for Biotechnology and Interdisciplinary Studies Rensselaer Polytechnic Institute Rensselaer Exploratory Center for Cheminformatics Research http://reccr.chem.rpi.edu/ Many thanks to Theresa Hepburn! Upcoming lecture plug: Prof. Curt Breneman DSES Department November 15th Advances in Cheminformatics: Applications in Biotechnology, Drug Design and Bioseparations Cheminformatics is about collecting, storing, and analyzing [usually large amounts of] chemical data. •Pharmaceutical research •Materials design •Computational/Automated techniques for analysis •Virtual high-throughput screening (VHTS) QSAR - quantitative structure-activity relationship Molecular Model Activity Structures O N N Cl AAACCTCATAGGAAGCA TACCAGGAATTACATCA … Molecular Model Activity Structures O N N Cl AAACCTCATAGGAAGCATACCA GGAATTACATCA… Structural Descriptors Physiochemical Descriptors Topological Descriptors Geometrical Descriptors Molecular Descriptors Model Activity Structures Acitivty: bioactivity, ADME/Tox evaluation, hERG channel effects, p-456 isozyme inhibition, anti- malarial efficacy, etc… Structural Descriptors Physiochemical Descriptors Topological Descriptors Molecule D1 D2 … Activity (IC50) molecule #1 21 0.1 Geometrical Descriptors = molecule #2 33 2.1 molecule #3 10 0.9 + … Activity Molecular Descriptors Model Activity Structures Goal: Minimize the error: ∑((yi −f xi)) f1(x) f2(x) Regression Models: linear, multi-linear,
    [Show full text]
  • Committee on Publications and Cheminformatics Data Standards (CPCDS)
    Committee on Publications and Cheminformatics Data Standards (CPCDS) Report to the Bureau, April 2020 Leah McEwen, Committee Chair I. Executive Summary The IUPAC Committee on Publications and Cheminformatics Data Standards (CPCDS) advises on issues related to dissemination of information, primarily of IUPAC outputs. The portfolio is expanding and diversifying, in types of content, modes of publication and potential readership. Publication of IUPAC recommendations, technical reports and other information resources remains at the core of IUPAC dissemination activity and that of CPCDS. Increasingly the committee also focuses on the development and dissemination of chemical data and information standards to facilitate robust communication in the digital environment. Further details on these two priority areas are provided in Appendix A (Subcommittee on Publications: Report to CPCDS) and Appendix B (CPCDS Chair’s Statement: Towards a “Digital IUPAC”). The 2018-2019 biennium brought stability to key systems and workflows including those supporting IUPAC’s flagship journal Pure and Applied Chemistry (PAC) and online access to the Compendium of Chemical Terminology (a.k.a., the “Gold Book”). A number of collaborative symposia and workshops with strategic partners including CODATA and the GO FAIR Chemistry Implementation Network (ChIN) surfaced use cases and infrastructure needs for digital science among diverse community stakeholders. These activities lay the groundwork for developing a robust and systematic program for Digital IUPAC and “the creation of a consistent and interoperable global framework for human and machine-readable chemical information,” as articulated in the CPCDS Terms of Reference. This vision will be a critical component of success for IUPAC’s contribution towards the United Nations Sustainable Development Goals.
    [Show full text]
  • Scalable Analysis of Large Datasets in Life Sciences
    Scalable Analysis of Large Datasets in Life Sciences LAEEQ AHMED Doctoral Thesis in Computer Science Stockholm, Sweden 2019 Division of Computational Science and Technology School of Electrical Engineerning and Computer Science KTH Royal Institute of Technology TRITA-EECS-AVL-2019:69 SE-100 44 Stockholm ISBN: 978-91-7873-309-5 SWEDEN Akademisk avhandling som med tillstånd av Kungl Tekniska högskolan framlägges till offentlig granskning för avläggande av teknologie doktorsexamen i elektrotek- nik och datavetenskap tisdag den 3:e Dec 2019, klockan 10.00 i sal Kollegiesalen, Brinellvägen 8, KTH-huset, Kungliga Tekniska Högskolan. © Laeeq Ahmed, 2019 Tryck: Universitetsservice US AB To my respected parents, my beloved wife and my lovely brother and sister Abstract We are experiencing a deluge of data in all fields of scientific and business research, particularly in the life sciences, due to the development of better instrumentation and the rapid advancements that have occurred in informa- tion technology in recent times. There are major challenges when it comes to handling such large amounts of data. These range from the practicalities of managing these large volumes of data, to understanding the meaning and practical implications of the data. In this thesis, I present parallel methods to efficiently manage, process, analyse and visualize large sets of data from several life sciences fields at a rapid rate, while building and utilizing various machine learning techniques in a novel way. Most of the work is centred on applying the latest Big Data Analytics frameworks for creating efficient virtual screening strategies while working with large datasets. Virtual screening is a method in cheminformatics used for Drug discovery by searching large libraries of molecule structures.
    [Show full text]
  • Cheminformatics for Genome-Scale Metabolic Reconstructions
    CHEMINFORMATICS FOR GENOME-SCALE METABOLIC RECONSTRUCTIONS John W. May European Molecular Biology Laboratory European Bioinformatics Institute University of Cambridge Homerton College A thesis submitted for the degree of Doctor of Philosophy June 2014 Declaration This thesis is the result of my own work and includes nothing which is the outcome of work done in collaboration except where specifically indicated in the text. This dissertation is not substantially the same as any I have submitted for a degree, diploma or other qualification at any other university, and no part has already been, or is currently being submitted for any degree, diploma or other qualification. This dissertation does not exceed the specified length limit of 60,000 words as defined by the Biology Degree Committee. This dissertation has been typeset using LATEX in 11 pt Palatino, one and half spaced, according to the specifications defined by the Board of Graduate Studies and the Biology Degree Committee. June 2014 John W. May to Róisín Acknowledgements This work was carried out in the Cheminformatics and Metabolism Group at the European Bioinformatics Institute (EMBL-EBI). The project was fund- ed by Unilever, the Biotechnology and Biological Sciences Research Coun- cil [BB/I532153/1], and the European Molecular Biology Laboratory. I would like to thank my supervisor, Christoph Steinbeck for his guidance and providing intellectual freedom. I am also thankful to each member of my thesis advisory committee: Gordon James, Julio Saez-Rodriguez, Kiran Patil, and Gos Micklem who gave their time, advice, and guidance. I am thankful to all members of the Cheminformatics and Metabolism Group.
    [Show full text]
  • Print Special Issue Flyer
    IMPACT FACTOR 4.411 an Open Access Journal by MDPI Cheminformatics, Past, Present, and Future: From Chemistry to Nanotechnology and Complex Systems Guest Editors: Message from the Guest Editors Prof. Dr. Humbert González- Cheminformatics techniques form have been interesting Díaz tools in the effort to explore large chemical spaces, [email protected] reducing at the same time animal testing as well as costs in Dr. Aliuska Duardo-Sanchez terms of materials and human resources on drug discovery [email protected] processes in medicinal chemistry. In addition, with the emergence of nanotechnologies, cheminformatics needs Prof. Dr. Alejandro Pazos to deal with drug–nanoparticle systems used as drug Sierra [email protected] delivery systems, drug co-therapy systems, etc. This brings to mind the application of cheminformatics in areas beyond drug discovery such as the fuel industry, polymer sciences, materials science, and biomedical engineering. Deadline for manuscript submissions: In this context, we propose to open this new issue to closed (15 November 2020) discuss with all colleagues worldwide all the past, present, and future challenges of cheminformatics. The present Special Issue is also associated with MOL2NET-05, the International Conference on Multidisciplinary Sciences, ISSN: 2624-5078, MDPI SciForum, Basel, Switzerland, 2019. The link of the conference: https://mol2net- 05.sciforum.net/. We especially encourage submissions of papers from colleagues worldwide to the conference (short communications) and complete versions (full papers) to the present Special Issue. mdpi.com/si/31770 SpeciaIslsue IMPACT FACTOR 4.411 an Open Access Journal by MDPI Editor-in-Chief Message from the Editor-in-Chief Prof.
    [Show full text]
  • Trust, but Verify: on the Importance of Chemical Structure Curation in Cheminformatics and QSAR Modeling Research
    J. Chem. Inf. Model. XXXX, xxx, 000 A Trust, But Verify: On the Importance of Chemical Structure Curation in Cheminformatics and QSAR Modeling Research Denis Fourches,† Eugene Muratov,†,‡ and Alexander Tropsha*,† Laboratory for Molecular Modeling, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, and Laboratory of Theoretical Chemistry, Department of Molecular Structure, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080, Ukraine Received May 5, 2010 1. INTRODUCTION to the prediction performances of the derivative QSAR models. They also presented several illustrative examples With the recent advent of high-throughput technologies of incorrect structures generated from either correct or for both compound synthesis and biological screening, there incorrect SMILES. The main conclusions of the study were is no shortage of publicly or commercially available data that small structural errors within a data set could lead to sets and databases1 that can be used for computational drug 2 significant losses of predictive ability of QSAR models. The discovery applications (reviewed recently in Williams et al. ). authors further demonstrated that manual curation of struc- Rapid growth of large, publicly available databases (such 3 4 tural data leads to substantial increase in the model predic- as PubChem or ChemSpider containing more than 20 tivity. This conclusion becomes especially important in light million molecular records each) enabled by experimental of the aforementioned study of Oprea et al.6 that cited a projects such as NIH’s Molecular Libraries and Imaging significant error rate in medicinal chemistry literature. Initiative5 provides new opportunities for the development Alarmed by these conclusions, we have examined several of cheminformatics methodologies and their application to popular public databases of bioactive molecules to assess knowledge discovery in molecular databases.
    [Show full text]
  • Chemoinformatics for Green Chemistry
    Contents LIST OF PAPERS .................................................................................... 3 LISTS OF ACRONYMS AND ABBREVIATIONS ............................... 4 INTRODUCTION .................................................................................. 7 BACKGROUND .............................................................................................7 AIMS OF THE THESIS ..................................................................................9 METHODOLOGY ............................................................................... 11 QUANTITATIVE STRUCTURE-PROPERTY RELATIONSHIPS (QSPR)......11 Linear Free Energy Relationships (LFER)...............................................11 Linear Solvation Energy Relationship (LSER).........................................12 Reversed-Phase Liquid Chromatography (RPLC).....................................14 Quantitative Structure-Retention Relationships (QSRR) .........................14 Global Linear Solvation Energy Relationship (LSER)..............................15 Molecular Descriptors ...............................................................................16 The Definition of the Applicability Domain ..............................................17 The Distance-Based Approach Using PLS.......................................17 The Williams Plot .............................................................................18 Associative Neural Networks (ASNN)..............................................19 DATA ANALYSIS ........................................................................................20
    [Show full text]
  • The Literature of Chemoinformatics: 1978–2018
    International Journal of Molecular Sciences Editorial The Literature of Chemoinformatics: 1978–2018 Peter Willett Information School, University of Sheffield, Sheffield S1 4DP, UK; p.willett@sheffield.ac.uk Received: 17 July 2020; Accepted: 31 July 2020; Published: 4 August 2020 Abstract: This article presents a study of the literature of chemoinformatics, updating and building upon an analogous bibliometric investigation that was published in 2008. Data on outputs in the field, and citations to those outputs, were obtained by means of topic searches of the Web of Science Core Collection. The searches demonstrate that chemoinformatics is by now a well-defined sub-discipline of chemistry, and one that forms an essential part of the chemical educational curriculum. There are three core journals for the subject: The Journal of Chemical Information and Modeling, the Journal of Cheminformatics, and Molecular Informatics, and, having established itself, chemoinformatics is now starting to export knowledge to disciplines outside of chemistry. Keywords: bibliometrics; cheminformatics; chemoinformatics; scientometrics 1. Introduction Increasing use is being made of bibliometric methods to analyze the published academic literature, with studies focusing on, e.g., author productivity, the articles appearing in a specific journal, the characteristics of bibliographic frequency distributions, new metrics for research evaluation, and the citations to publications in a specific subject area inter alia (e.g., [1–6]). There have been many bibliometric studies of various aspects of chemistry over the years, with probably the earliest such study being the famous 1926 paper by Lotka in which he discussed author productivity based in part on an analysis of publications in Chemical Abstracts [7].
    [Show full text]
  • Cheminformatics-Aided Pharmacovigilance: Application To
    Low Y. et al. J Am Med Inform Assoc 2015;0:1–11. doi:10.1093/jamia/ocv127, Research and Applications Journal of the American Medical Informatics Association Advance Access published October 24, 2015 RECEIVED 27 March 2015 Cheminformatics-aided pharmacovigilance: REVISED 6 July 2015 ACCEPTED 11 July 2015 application to Stevens-Johnson Syndrome Yen S Low1,2, Ola Caster3,4, Tomas Bergvall3, Denis Fourches1, Xiaoling Zang1, G Niklas Nore´n3,5, Ivan Rusyn2, Ralph Edwards3, Alexander Tropsha1 ABSTRACT .................................................................................................................................................... Objective Quantitative Structure-Activity Relationship (QSAR) models can predict adverse drug reactions (ADRs), and thus provide early warnings of potential hazards. Timely identification of potential safety concerns could protect patients and aid early diagnosis of ADRs among the exposed. Our objective was to determine whether global spontaneous reporting patterns might allow chemical substructures associated with Stevens- Johnson Syndrome (SJS) to be identified and utilized for ADR prediction by QSAR models. RESEARCH AND APPLICATIONS Materials and Methods Using a reference set of 364 drugs having positive or negative reporting correlations with SJS in the VigiBase global repo- sitory of individual case safety reports (Uppsala Monitoring Center, Uppsala, Sweden), chemical descriptors were computed from drug molecular structures. Random Forest and Support Vector Machines methods were used to develop QSAR models, which were validated by external 5-fold cross validation. Models were employed for virtual screening of DrugBank to predict SJS actives and inactives, which were corroborated using Downloaded from knowledge bases like VigiBase, ChemoText, and MicroMedex (Truven Health Analytics Inc, Ann Arbor, Michigan). Results We developed QSAR models that could accurately predict if drugs were associated with SJS (area under the curve of 75%–81%).
    [Show full text]
  • The Tonnabytes Big Data Challenge
    The Tonnabytes Big Data Challenge: Transforming Science and Education Kirk Borne George Mason University Ever since we first began to explore our world… … humans have asked questions and … … have collected evidence (data) to help answer those questions. Astronomy: the world’s second oldest profession ! Characteristics of Big Data • Big quantities of data are acquired everywhere now. But… • What do we mean by “big”? • Gigabytes? Terabytes? Petabytes? Exabytes? • The meaning of “big” is domain-specific and resource- dependent (data storage, I/O bandwidth, computation cycles, communication costs) • I say … we all are dealing with our own “tonnabytes” • There are 4 dimensions to the Big Data challenge: 1. Volume (tonnabytes data challenge) 2. Complexity (variety, curse of dimensionality) 3. Rate of data and information flowing to us (velocity) 4. Verification (verifying inference-based models from data) • Therefore, we need something better to cope with the data tsunami … Data Science – Informatics – Data Mining Examples of Recommendations**: Inference from Massive or Complex Data • Advances in fundamental mathematics and statistics are needed to provide the language, structure, and tools for many needed methodologies of data-enabled scientific inference. – Example : Machine learning in massive data sets • Algorithmic advances in handling massive and complex data are crucial. • Visualization (visual analytics) and citizen science (human computation or data processing) will play key roles. • ** From the NSF report: Data-Enabled Science in the Mathematical and Physical Sciences, (2010) http://www.cra.org/ccc/docs/reports/DES-report_final.pdf This graphic says it all … • Clustering – examine the data and find the data clusters (clouds), without considering what the items are = Characterization ! • Classification – for each new data item, try to place it within a known class (i.e., a known category or cluster) = Classify ! • Outlier Detection – identify those data items that don’t fit into the known classes or clusters = Surprise ! Graphic provided by Professor S.
    [Show full text]