The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data

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Data Mining: Concepts 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

Data Mining - Concepts and Techniques.

Case-based reasoning is a recent approach to problem solving and learning that has got a lot of attention over the last few years. Originating in the US, the basic idea and underlying theories have spread to other continents, and we are now within a period of highly active research in case-based reasoning in Europe, as well. This paper gives an overview of the foundational issues related to case-based reasoning, describes some of the leading methodological approaches within the field, and exemplifies the current state through pointers to some systems. Initially, a general framework is defined, to which the subsequent descriptions and discussions will refer. The framework is influenced by recent methodologies for knowledge level descriptions of intelligent systems. The methods for case retrieval, reuse, solution testing, and learning are summarized, and their actual realization is discussed in the light of a few example systems that represent different CBR approaches. We also discuss the role of case-based methods as one type of reasoning and learning method within an integrated system architecture.

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Case-based reasoning: foundational issues, methodological variations, and system approaches

In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Thinking fast and slow.

Case-Based Reasoning Research and Development

A case in case-based reasoning is a contextualized piece of experience, which can be represented in various forms. Traditional approaches can be classified into three main categories: feature vector representations, structured representations, and textual representations. More sophisticated approaches make use of hierarchical representations or generalized cases. For particular tasks such as design and planning highly specific representations have been developed.

https://www.iiia.csic.es/~enric/papers/Representation_in_CBR.pdf

Representation in case-based reasoning

Problem-solving methods provide reusable architectures and components for implementing the reasoning part of knowledge-based systems. The Unified Problem-Solving Method Description Language (UPML) has been developed to describe and implement such architectures and components to facilitate their semi-automatic reuse and adaptation. In a nutshell, UPML is a framework for developing knowledge-intensive reasoning systems based on libraries of generic problem-solving components. The paper describes the components and adapters, architectural constraints, development guidelines, and tools provided by UPML. UPML is developed as part of the IBROW project, which provides an Internet-based brokering service for reusing problem-solving methods.

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The unified problem-solving method development language UPML

Overfitting is often considered the central problem in machine learning and data mining. When good performance on training data is not enough to reliably predict good generalization, researchers and practitioners often invoke ”Occam’s razor” to select among hypotheses: prefer the simplest hypothesis consistent with the data. Occam’s razor has a long history in science, but a mass of recent evidence suggests that in most cases it is outperformed by methods that deliberately produce more complex models. The poor performance of Occam’s razor can be largely traced to its failure to account for the search process by which hypotheses are obtained: by effectively assuming that the hypothesis space is exhaustively searched, complexity-based methods tend to over-penalize large spaces. This talk describes how information about the search process can be taken into account when evaluating hypotheses. The expected generalization error of a hypothesis is computed as a function of the search steps leading to it. Two variations of this ”processoriented” approach have yielded significant improvements in the accuracy of a rule learner. Process-oriented evaluation leads to the seemingly paradoxical conclusion that the same hypothesis will have different expected generalization errors depending on how it was generated. I believe that this is as it should be, and that a corresponding shift in our way of thinking about inductive learning is required.

Enric Plaza

Papers

Case-based reasoning: foundational issues, methodological variations, and system approaches

Case-Based Reasoning Research and Development

Representation in case-based reasoning

The unified problem-solving method development language UPML

Machine Learning: ECML 2000