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

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 Practical Machine Learning Tools and Techniques

The concept of an agent has become important in both Artificial Intelligence (AI) and mainstream computer science. Our aim in this paper is to point the reader at what we perceive to be the most important theoretical and practical issues associated with the design and construction of intelligent agents. For convenience, we divide these issues into three areas (though as the reader will see, the divisions are at times somewhat arbitrary). Agent theory is concerned with the question of what an agent is, and the use of mathematical formalisms for representing and reasoning about the properties of agents. Agent architectures can be thought of as software engineering models of agents;researchers in this area are primarily concerned with the problem of designing software or hardware systems that will satisfy the properties specified by agent theorists. Finally, agent languages are software systems for programming and experimenting with agents; these languages may embody principles proposed by theorists. The paper is not intended to serve as a tutorial introduction to all the issues mentioned; we hope instead simply to identify the most important issues, and point to work that elaborates on them. The article includes a short review of current and potential applications of agent technology.

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Intelligent Agents: Theory and Practice

The join operation is one of the most frequently used and expensive query processing operations in relational database systems. One method of joining two relations is to use a hash-based join algorithm. Hash-based join algorithms typically have two phases, a partitioning phase and a partition joining phase. We describe how an optimal multi-attribute hash (MAH) indexing scheme can be used to reduce the average cost of the partitioning phase of any hash-based join algorithm, by eliminating the partitioning phase entirely for many of the most common join queries. We demonstrate that the technique can be extended to include multiple copies of the data le, each with a diierent organization of the MAH indexing scheme, and that this further reduces the average cost of performing the partitioning phase of the hash join algorithm. We describe a relatively inexpensive method for determining a good MAH indexing scheme. Our experiments show that the schemes found using this method are usually optimal and performs the partitioning phase of the hash join algorithm at least three times faster than using the standard approach. We show that a signiicant change in the query pattern is required for a reorganization of the data le to be necessary, and show that reorganizing the data le is an inexpensive operation.

Using Optimized Multi-Attribute Hash Indexes for Hash Joins.

Distributed computing systems and applications are not only changing the face of computing, they're also continually changing the way we live, work, and interact as a society. In recent years, the demand for educational courses in advanced computing and networking has expanded rapidly. This has led to a huge demand for Internet-based distributed computing technologies (such as Web and grid services) and applications that virtualize geographically distributed resources to enable the creation of virtual enterprises, marketplaces, and service-oriented computing industries. Because current academic programs don't yet address the skill set required to meet these demands, we launched a Master of Engineering in distributed computing program at the University of Melbourne

An Innovative Master's Program in Distributed Computing

Computational diagnosis of cancer is a classification problem, and it has two special requirements on a learning algorithm: perfect accuracy and small number of features used in the classifier. This paper presents our results on an ovarian cancer data set. This data set is described by 15154 features, and consists of 253 samples. Each sample is referred to a woman who suffers from ovarian cancer or who does not have. In fact, the raw data is generated by the so-called mass spectrosmetry technology measuring the intensities of 15154 protein or peptide-features in a blood sample for every woman. The purpose is to identify a small subset of the features that can be used as biomarkers to separate the two classes of samples with high accuracy. Therefore, the identified features can be potentially used in routine clinical diagnosis for replacing labour-intensive and expensive conventional diagnosis methods. Our new tree-based method can achieve the perfect 100% accuracy in 10-fold cross validation on this data set. Meanwhile, this method also directly outputs a small set of biomarkers. Then we explain why support vector machines, naive bayes, and k-nearest neighbour cannot fulfill the purpose. This study is also aimed to elucidate the communication between contemporary cancer research and data mining techniques.

A Tree-Based Approach to the Discovery of Diagnostic Biomarkers for Ovarian Cancer

A novel automatic system for extracting the semantic descriptions of medical image content and concept in text form is presented. We first extract and analyze image features and the features are mapped to semantic descriptions by fuzzy functions. Based on these semantic descriptions, our system facilitates knowledge base construction using a machine learning scheme. The result would be useful for other researchers in medical image retrieval area, who can take advantage of both text-based queries and image queries.

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Automatic extraction of semantic concepts in medical images

A Stream AND-Parallel Execution Algorithm with Backtracking.

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