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

From the Publisher:
Written by the author of the best-selling HyperText & HyperMedia, this book provides an excellent guide to the methods of usability engineering. Special features: emphasizes cost-effective methods that will help developers improve their user interfaces immediately, shows you how to avoid the four most frequently listed reasons for delay in software projects, provides step-by-step information about which methods to use at various stages during the development life cycle, and offers information on the unique issues relating to informational usability. You do not need to have previous knowledge of usability to implement the methods provided, yet all of the latest research is covered.

Usability Engineering

Publisher Summary Computers are becoming the vehicle for an increasing range of everyday activities. Acquisition of news and information, mail and even social interactions and entertainment have become more and more computer based. These technological developments are not in line with a change in the way people interact with computers. Techniques from the field of AI, in particular so-called autonomous agents, can be used to implement a complementary style of interaction, which has been referred to as indirect management. Agents assist users in a range of different ways. They hide the complexity of difficult tasks, they perform tasks on the user's behalf, they can train or teach the user, they help different users collaborate, and they monitor events and procedures. The chapter focuses on an approach to building interface agents. It presents results from several prototype agents that have been built using this approach, including agents that provide personalized assistance with meeting scheduling, email handling, electronic news filtering, and selection of entertainment.

Agents that reduce work and information overload

Explicitly stated program invariants can help programmers by identifying program properties that must be preserved when modifying code. In practice, however, these invariants are usually implicit. An alternative to expecting programmers to fully annotate code with invariants is to automatically infer likely invariants from the program itself. This research focuses on dynamic techniques for discovering invariants from execution traces. This article reports three results. First, it describes techniques for dynamically discovering invariants, along with an implementation, named Daikon, that embodies these techniques. Second, it reports on the application of Daikon to two sets of target programs. In programs from Gries's work (1981) on program derivation, the system rediscovered predefined invariants. In a C program lacking explicit invariants, the system discovered invariants that assisted a software evolution task. These experiments demonstrate that, at least for small programs, invariant inference is both accurate and useful. Third, it analyzes scalability issues, such as invariant detection runtime and accuracy, as functions of test suites and program points instrumented.

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Dynamically discovering likely program invariants to support program evolution

Usability Engineering: Scenario-Based Development of Human-Computer Interaction is a radical departure from traditional books that emphasize theory and address experts. This book focuses on the realities of product development, showing how user interaction scenarios can make usability practices an integral part of interactive system development. As you'll learn, usability engineering is not the application of inflexible rules; it's a process of analysis, prototyping, and problem solving in which you evaluate tradeoffs, make reasoned decisions, and maximize the overall value of your product.

Usability Engineering: Scenario-Based Development of Human-Computer Interaction

Part 1 Systems: Pygmalion tinker a predictive calculator rehearsal world smallStar peridot metamouse TELS eager garnet the Turvy experience chimera the geometer's sketchpad tourmaline a history-based macro by example system mondrian triggers the AIDE project. Part 2 Components: a history of editable graphical histories graphical representation and feedback in a PBD system PBD invocation techniques a system-wide macro facility based on aggregate events making programming accessible to visual problem solvers using voice input to disambiguate intent. Part 3 Perspectives: characterizing PBD systems demonstrational interfaces just-in-time programming.

Watch what I do: programming by demonstration

Turvy is a simulated prototype of an instructible agent. The user teaches it by demonstrating actions and pointing at or talking about relevant data. We formalized our assumptions about what could be implemented, then used the Wizard of Oz to flesh out a design and observe users' reactions as they taught several editing tasks. We found: a) all users invent a similar set of commands to teach the agent; b) users learn the agent's language by copying its speech; c) users teach simple tasks with ease and complex ones with reasonable effort; and d) agents cannot expect users to point to or identify critical features without prompting. In conducting this rather complex simulation, we learned some lessons about using the Wizard of Oz to prototype intelligent agents: a) design of the simulation benefits greatly from prior implementation experience; b) the agent's behavior and dialog capabilities must be based on formal models; c) studies of verbal discourse lead directly to an implementable system; d) the designer benefits greatly by becoming the Wizard; and e) qualitative data is more valuable for answering global concerns, while quantitative data validates accounts and answers fine-grained questions.

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Prototyping an intelligent agent through Wizard of Oz

Metamouse is a device enabling the user of a drawing program to specify graphical procedures by supplying example execution traces. The user manipulates objects directly on the screen, creating graphical tools where necessary to help make constraints explicit; the system records the sequence of actions and induces a procedure. Generalization is used both to identify the key features of individual program steps, disregarding coincidental events; and to connect the steps into a program graph, creating loops and conditional branches as appropriate. Metamouse operates within a 2D click-and-drag drafting package, and incorporates a strong model of the relative importance of different types of graphical constraint. Close attention is paid to user interface aspects, and Metamouse helps the user by predicting and performing actions, thus reducing the tedium of repetitive graphical editing tasks.

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Metamouse: specifying graphical procedures by example

The paper describes a technique that constructs models of symbol sequences in the form of small, human-readable, hierarchical grammars. The grammars are both semantically plausible and compact. The technique can induce structure from a variety of different kinds of sequence, and examples are given of models derived from English text, C source code and a sequence of terminal control codes. It explains the grammatical induction technique, demonstrates its application to three very different sequences, evaluates its compression performance, and concludes by briefly discussing its use as a method for knowledge acquisition. >

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Compression by induction of hierarchical grammars

End users who need to program within highly interactive direct-manipulation interfaces should be able to communicate their intentions through concrete demonstration rather than in terms of symbolic abstraction. This paper describes a system that learns procedures in interactive graphics taught to it “by example” by minimally trained users. It shows how techniques of machine learning and reactive interfaces can support one another—the former providing generalization heuristics to identify constraints implicit in user actions, the latter offering immediate feedback to help the user clarify hidden constraints and correct errors before they are planted into the procedure. The teacher's attention is focused on the learning system's perceptual and inferential shortcomings through a metaphorical apprentice called Metamouse, which generalizes action sequences on the fly and eagerly carries out any actions it can predict. The success of the induction process is assessed quantitatively by counting erroneous predictions made during example tasks.

David L. Maulsby

Papers

Watch what I do: programming by demonstration

Prototyping an intelligent agent through Wizard of Oz

Metamouse: specifying graphical procedures by example

Compression by induction of hierarchical grammars

Inducing programs in a direct-manipulation environment