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

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.

/pdf/dynamically-discovering-likely-program-invariants-to-support-2hrrlyjk0k.pdf

Dynamically discovering likely program invariants to support program evolution

The use of hierarchy is an important component of object-oriented design. Hierarchy allows the use of type families, in which higher level supertypes capture the behavior that all of their subtypes have in common. For this methodology to be effective, it is necessary to have a clear understanding of how subtypes and supertypes are related. This paper takes the position that the relationship should ensure that any property proved about supertype objects also holds for its subtype objects. It presents two ways of defining the subtype relation, each of which meets this criterion, and each of which is easy for programmers to use. The subtype relation is based on the specifications of the sub- and supertypes; the paper presents a way of specifying types that makes it convenient to define the subtype relation. The paper also discusses the ramifications of this notion of subtyping on the design of type families.

A behavioral notion of subtyping

This thesis defines a set of program restructuring operations (refactorings) that support the design, evolution and reuse of object-oriented application frameworks.
The focus of the thesis is on automating the refactorings in a way that preserves the behavior of a program. The refactorings are defined to be behavior preserving, provided that their preconditions are met. Most of the refactorings are simple to implement and it is almost trivial to show that they are behavior preserving. However, for a few refactorings, one or more of their preconditions are in general undecidable. Fortunately, for some cases it can be determined whether these refactorings can be applied safely.
Three of the most complex refactorings are defined in detail: generalizing the inheritance hierarchy, specializing the inheritance hierarchy and using aggregations to model the relationships among classes. These operations are decomposed into more primitive parts, and the power of these operations is discussed from the perspectives of automatability and usefulness in supporting design.
Two design constraints needed in refactoring are class invariants and exclusive components. These constraints are needed to ensure that behavior is preserved across some refactorings. This thesis gives some conservative algorithms for determining whether a program satisfies these constraints, and describes how to use this design information to refactor a program.

Refactoring object-oriented frameworks

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

Programming by example is a way of programming a software system in its own user interface. The user of the system writes a program by giving an example of what the program should do. The system records the sequence of actions, and can perform it again. Programming by example allows a user to create programs without doing conventional programming. Programming by example was incorporated into a simulation of an office information system. As the system evolved, it became clear that the basic concept of programming by example needed to be extended: certain aspects of program creation are better done by program modification than by using the programming-by-example mechanism. The final system includes a static program representation that is easy to understand, a mechanism for describing data, and a method of adding control structure. A user operates on certain data while creating a program by example, but does not necessarily tell the system why that data was selected. The user can supply this missing information by using data descriptions, which are program operands that specify how to choose the data to be used when the program is run. The system automatically supplies reasonable default data descriptions while recording a program; if necessary the user can modify the descriptions later to reflect his or her true intentions. Since programming by example is best at recording sequential user actions, rather than branching or iteration, control structure that alters program flow is specified by program editing. The operands in iterations and the predicates in conditional control structures are built from data descriptions. Real users have learned to use the system quickly and with little difficulty. The techniques used in this system can be applied to other systems as well. This research has demonstrated that programming by example is a practical method for creating programs. This document is a slightly corrected version of a dissertation submitted to the Department of Electrical Engineering and Computer Sciences, Computer Science Division, University of California, Berkeley, in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Computer Science. A nearly identical version of this document was also published by the Office Systems Division, Systems Development Department, Xerox Corporation, as technical report OSD-T8402, December, 1984. An online version of this document was recreated by the author in March, 1999. For more information, contact the author at halbert@world.std.com halbert@halwitz.org. Copyright © 1984 by Daniel Conrad Halbert. All rights reserved.

/pdf/programming-by-example-1rh8nc4pdx.pdf

Programming by example

To be a good object-oriented programmer, you should use types and inheritance wisely. ThiS tutorial will help you do so.

Using Types and Inheritance in Object-Oriented Programming

The Trellis programming environment supports programming in Trellis/Owl, an object-based language with multiple inheritance and compile-time type-checking. Trellis is composed of a number of integrated tools that share a common programming environment database. It is a highly interactive, easy-to-use programming environment, providing various programming aids, incremental compilation, and good debugging support. Trellis is both integrated and open-ended.Trellis was specifically designed to support the object-oriented programming methodology. Thus it provides tools to manage the use of types and inheritance. Trellis takes advantage of the strong-typing features of the Trellis/Owl language to provide more support for the programmer by keeping track of cross-references and inconsistencies in code.

The Trellis programming environment

If the object-oriented style of programming hopes to live up to its potential as an improved methodology for software programming, a clear understanding of how to use types and inheritance is essential. Our experiences with using object-oriented languages and teaching object-oriented techniques to other programmers have shown that effective use of types and inheritance may be problematic. There are no concrete guidelines to assist programmers, and the existing aphorisms often create interpretation problems for novice object-oriented programmers. In this paper we look at how types, subtyping, and inheritance are used in object-oriented languages. We discuss the different ways that types and type hierarchies can be used to structure programs. We illustrate appropriate use of these concepts through examples and develop guidelines to assist programmers in using the object-oriented methodology effectively.

Daniel C. Halbert

Papers

Watch what I do: programming by demonstration

Programming by example

Using Types and Inheritance in Object-Oriented Programming

The Trellis programming environment

Using types and inheritance in object-oriented languages