Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. *Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

Data Mining: Practical Machine Learning Tools and Techniques

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Contents Foreword Preface to the third edition Preface to the second edition Preface to the first edition Introduction Part 1 Foundations Chapter 1 The human 1.1 Introduction 1.2 Input-output channels Design Focus: Getting noticed Design Focus: Where's the middle? 1.3 Human memory Design Focus: Cashing in Design Focus: 7 +- 2 revisited 1.4 Thinking: reasoning and problem solving Design Focus: Human error and false memories 1.5 Emotion 1.6 Individual differences 1.7 Psychology and the design of interactive systems 1.8 Summary Exercises Recommended reading Chapter 2 The computer 2.1 Introduction Design Focus: Numeric keypads 2.2 Text entry devices 2.3 Positioning, pointing and drawing 2.4 Display devices Design Focus: Hermes: a situated display 2.5 Devices for virtual reality and 3D interaction 2.6 Physical controls, sensors and special devices Design Focus: Feeling the road Design Focus: Smart-Its - making sensors easy 2.7 Paper: printing and scanning Design Focus: Readability of text 2.8 Memory 2.9 Processing and networks Design Focus: The myth of the infinitely fast machine 2.10 Summary Exercises Recommended reading Chapter 3 The interaction 3.1 Introduction 3.2 Models of interaction Design Focus: Video recorder 3.3 Frameworks and HCI 3.4 Ergonomics Design Focus: Industrial interfaces 3.5 Interaction styles Design Focus: Navigation in 3D and 2D 3.6 Elements of the WIMP interface Design Focus: Learning toolbars 3.7 Interactivity 3.8 The context of the interaction Design Focus: Half the picture? 3.9 Experience, engagement and fun 3.10 Summary Exercises Recommended reading Chapter 4 Paradigms 4.1 Introduction 4.2 Paradigms for interaction 4.3 Summary Exercises Recommended reading Part 2 Design process Chapter 5 Interaction design basics 5.1 Introduction 5.2 What is design? 5.3 The process of design 5.4 User focus Design Focus: Cultural probes 5.5 Scenarios 5.6 Navigation design Design Focus: Beware the big button trap Design Focus: Modes 5.7 Screen design and layout Design Focus: Alignment and layout matter Design Focus: Checking screen colors 5.8 Iteration and prototyping 5.9 Summary Exercises Recommended reading Chapter 6 HCI in the software process 6.1 Introduction 6.2 The software life cycle 6.3 Usability engineering 6.4 Iterative design and prototyping Design Focus: Prototyping in practice 6.5 Design rationale 6.6 Summary Exercises Recommended reading Chapter 7 Design rules 7.1 Introduction 7.2 Principles to support usability 7.3 Standards 7.4 Guidelines 7.5 Golden rules and heuristics 7.6 HCI patterns 7.7 Summary Exercises Recommended reading Chapter 8 Implementation support 8.1 Introduction 8.2 Elements of windowing systems 8.3 Programming the application Design Focus: Going with the grain 8.4 Using toolkits Design Focus: Java and AWT 8.5 User interface management systems 8.6 Summary Exercises Recommended reading Chapter 9 Evaluation techniques 9.1 What is evaluation? 9.2 Goals of evaluation 9.3 Evaluation through expert analysis 9.4 Evaluation through user participation 9.5 Choosing an evaluation method 9.6 Summary Exercises Recommended reading Chapter 10 Universal design 10.1 Introduction 10.2 Universal design principles 10.3 Multi-modal interaction Design Focus: Designing websites for screen readers Design Focus: Choosing the right kind of speech Design Focus: Apple Newton 10.4 Designing for diversity Design Focus: Mathematics for the blind 10.5 Summary Exercises Recommended reading Chapter 11 User support 11.1 Introduction 11.2 Requirements of user support 11.3 Approaches to user support 11.4 Adaptive help systems Design Focus: It's good to talk - help from real people 11.5 Designing user support systems 11.6 Summary Exercises Recommended reading Part 3 Models and theories Chapter 12 Cognitive models 12.1 Introduction 12.2 Goal and task hierarchies Design Focus: GOMS saves money 12.3 Linguistic models 12.4 The challenge of display-based systems 12.5 Physical and device models 12.6 Cognitive architectures 12.7 Summary Exercises Recommended reading Chapter 13 Socio-organizational issues and stakeholder requirements 13.1 Introduction 13.2 Organizational issues Design Focus: Implementing workflow in Lotus Notes 13.3 Capturing requirements Design Focus: Tomorrow's hospital - using participatory design 13.4 Summary Exercises Recommended reading Chapter 14 Communication and collaboration models 14.1 Introduction 14.2 Face-to-face communication Design Focus: Looking real - Avatar Conference 14.3 Conversation 14.4 Text-based communication 14.5 Group working 14.6 Summary Exercises Recommended reading Chapter 15 Task analysis 15.1 Introduction 15.2 Differences between task analysis and other techniques 15.3 Task decomposition 15.4 Knowledge-based analysis 15.5 Entity-relationship-based techniques 15.6 Sources of information and data collection 15.7 Uses of task analysis 15.8 Summary Exercises Recommended reading Chapter 16 Dialog notations and design 16.1 What is dialog? 16.2 Dialog design notations 16.3 Diagrammatic notations Design Focus: Using STNs in prototyping Design Focus: Digital watch - documentation and analysis 16.4 Textual dialog notations 16.5 Dialog semantics 16.6 Dialog analysis and design 16.7 Summary Exercises Recommended reading Chapter 17 Models of the system 17.1 Introduction 17.2 Standard formalisms 17.3 Interaction models 17.4 Continuous behavior 17.5 Summary Exercises Recommended reading Chapter 18 Modeling rich interaction 18.1 Introduction 18.2 Status-event analysis 18.3 Rich contexts 18.4 Low intention and sensor-based interaction Design Focus: Designing a car courtesy light 18.5 Summary Exercises Recommended reading Part 4 Outside the box Chapter 19 Groupware 19.1 Introduction 19.2 Groupware systems 19.3 Computer-mediated communication Design Focus: SMS in action 19.4 Meeting and decision support systems 19.5 Shared applications and artifacts 19.6 Frameworks for groupware Design Focus: TOWER - workspace awareness Exercises Recommended reading Chapter 20 Ubiquitous computing and augmented realities 20.1 Introduction 20.2 Ubiquitous computing applications research Design Focus: Ambient Wood - augmenting the physical Design Focus: Classroom 2000/eClass - deploying and evaluating ubicomp 20.3 Virtual and augmented reality Design Focus: Shared experience Design Focus: Applications of augmented reality 20.4 Information and data visualization Design Focus: Getting the size right 20.5 Summary Exercises Recommended reading Chapter 21 Hypertext, multimedia and the world wide web 21.1 Introduction 21.2 Understanding hypertext 21.3 Finding things 21.4 Web technology and issues 21.5 Static web content 21.6 Dynamic web content 21.7 Summary Exercises Recommended reading References Index

Human-Computer Interaction

: Successful Software reuse requires the systematic discovery and exploitation of commonality across related software systems. By examining related software systems and the underlying theory of the class of systems they represent, domain analysis can provide a generic description of the requirements of that class of systems and a set of approaches for their implementation. This report will establish methods for performing a domain analysis and describe the products of the domain analysis process. To illustrate the application of domain analysis to a representative class of software systems, this report will provide a domain analysis of window management system software.

/pdf/feature-oriented-domain-analysis-foda-feasibility-study-2zsj0a30fo.pdf

Feature-Oriented Domain Analysis (FODA) Feasibility Study

How to ... be a reflective practitioner.

Intelligence in Demonstrational Interfaces.

Cilk Plus and OpenMP are parallel language extensions for the C and C++ programming languages. The CPLEX Study Group of the ISO/IEC C Standards Committee is developing a proposal for a parallel programming extension to C that combines ideas from Cilk Plus and OpenMP. We conducted a preliminary comparison of Cilk Plus and OpenMP in a master's level course on security to evaluate the design tradeoffs in the usability and security of these two approaches. The eventual goal is to inform decision-making within the committee. We found several usability problems worthy of further investigation based on student performance, including declaring and using reductions, multi-line compiler directives, and the understanda-bility of task assignment to threads.

/pdf/a-course-based-usability-analysis-of-cilk-plus-and-openmp-391qanv3mq.pdf

A course-based usability analysis of Cilk Plus and OpenMP

We present a new visual programming language and environment that serves as a form of feedback and representation in a Programming by Demonstration system. The language differs from existing visual languages because it explicitly represents data objects and implicitly represents operations by changes in data objects. The system was designed to provide non-programmers with programming support for common, repetitive tasks and incorporates some principles of cognition to assist these users in learning to use it. With this in mind, we analyze the language and its editor along cognitive dimensions. The assessment provides insight into both strengths and weaknesses of the system, suggesting a number of design changes.

Pursuit: Visual Programming in a Visual Domain

End users create software whenever they write, for instance, educational simulations, spreadsheets, or dynamic e-business web applications. Researchers are working to bring the benefits of rigorous software engineering methodologies to these end users to try to make their software more reliable. Unfortunately, errors are pervasive in end-user software, and the resulting impact is sometimes enormous. This special interest group meeting will bring together the community of researchers who are addressing this topic with the companies that are creating and using end-user programming tools.

End user software engineering: CHI: 2009 special interest group meeting

Software created by end users often lacks key quality attributes that professional programmers try to ensure through the use of abstraction. Yet to date, large-scale studies of end users have not examined end user software usage at a level which is sufficiently fine-grained to determine the extent to which they create abstractions. To address this, we deployed an online survey to Information Week subscribers to ask about not only software usage but also feature usage related to abstraction creation. Most respondents did create abstractions. Moreover, through factor analysis, we found that features fell into three clusters–when users had a propensity to use one feature, then they also had a propensity to use other features in the same cluster. These clusters corresponded to macro features, linked data structure features, and imperative features. For each of the three factors, we created a scale and used it to categorize users into two bins—those with a high propensity to use features associated with that scale, and those with a low propensity. Compared to users with a low propensity to use imperative features, users with a high propensity to use imperative features were more likely to report testing and documenting. Propensity to use linked structure features was less strongly related to these practices. These findings represent a step toward a more complete map of end users’ skills. This work has been funded in part by the EUSES Consortium via the National Science Foundation (ITR0325273), by the National Science Foundation under Grant CCF-0438929, by the Sloan Software Industry Center at Carnegie Mellon, and by the High Dependability Computing Program from NASA Ames cooperative agreement NCC-2-1298. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the sponsors.

https://faculty.washington.edu/ajko/papers/Scaffidi2005IdentifyingEndUsers.pdf

Identifying categories of end users based on the abstractions that they create

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Brad A. Myers

Author Tools

Chat about Author