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: 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

Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.

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The Structure and Function of Complex Networks

The importance of a Web page is an inherently subjective matter, which depends on the readers interests, knowledge and attitudes. But there is still much that can be said objectively about the relative importance of Web pages. This paper describes PageRank, a mathod for rating Web pages objectively and mechanically, effectively measuring the human interest and attention devoted to them. We compare PageRank to an idealized random Web surfer. We show how to efficiently compute PageRank for large numbers of pages. And, we show how to apply PageRank to search and to user navigation.

The PageRank Citation Ranking : Bringing Order to the Web

The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such clusters, or communities, can be considered as fairly independent compartments of a graph, playing a similar role like, e. g., the tissues or the organs in the human body. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. This problem is very hard and not yet satisfactorily solved, despite the huge effort of a large interdisciplinary community of scientists working on it over the past few years. We will attempt a thorough exposition of the topic, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.

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Community detection in graphs

Embodiments may be implemented by a computing device, such as a head-mountable display or mobile phone, in order to pre-emptively warm up the device's camera, when it is probable that a user will be taking a photo. An illustrative method involves a computing device (a) receiving sensor data from one or more sensors associated with the computing device, wherein the computing device comprises an image-capture device, (b) analyzing the sensor data to detect at least one pre-image-capture signal, wherein the at least one pre-image-capture signal indicates a subsequent image-capture signal is likely to be received, and (c) in response to detecting the at least one pre-image-capture signal, causing the computing device to initiate an image-capture preparation process that prepares the image-capture device to capture an image.

Preparation of image capture device in response to pre-image-capture signal

An exemplary computer-implemented method involves a computing device: (a) causing a graphic display to display a user-interface for accessing one or more experience-sharing sessions, wherein the user-interface is associated with a first user-account; (b) determining a location; (c) initiating a location-based search for one or more associated experience-sharing sessions, wherein each associated experience sharing session involves at least one second user-account that has a confirmed association with the first user-account, and wherein the location-based search is based on the determined location; and (d) causing the graphic display to display at least a portion of each of one or more associated experience-sharing sessions that result from the location-based search.

Experience sharing in location-based social networking

Embodiments of a tiled multi-panel display including first and second display panels, each including a substrate with a main portion having a main thickness, an abutting edge with an thickness less than the main thickness, and a taper surface extending between the main portion and the abutting edge, and an array of display pixels disposed in the main portion and extending at least partially around the curved surface. The abutting edge of the first display panel abuts the abutting edge of the second display panel to form a seam, and a seam-concealing optical element is disposed in a void formed by the first and second taper surfaces. Other embodiments are disclosed and claimed.

Display panel tiling using seam-concealing optics

A high-altitude balloon is provided that may use at least one conductive coil to facilitate attitude control of the balloon. The balloon may include an envelope, a conductive coil, and a control system. The conductive coil may be operatively coupled to the envelope, and the control system may be configured to perform functions. The functions may include determining a measure of rotation with respect to a predetermined orientation of the balloon. The functions may additionally include causing a current to be applied to the conductive coil in a manner such that torque is applied to the envelope that counteracts the measure of rotation and substantially returns the balloon to the predetermined orientation.

Attitude control for a high-altitude balloon

A system provides a periodically changing story line and/or a special event company logo to entice users to access a web page. For the story line, the system may receive objects that tell a story according to the story line and successively provide the objects on the web page for predetermined or random amounts of time. For the special event company logo, the system may modify a standard company logo for a special event to create a special event logo, associate one or more search terms with the special event logo, and upload the special event logo to the web page. The system may then receive a user selection of the special event logo and provide search results relating to the special event.

Sergey Brin

Papers

Preparation of image capture device in response to pre-image-capture signal

Experience sharing in location-based social networking

Display panel tiling using seam-concealing optics

Attitude control for a high-altitude balloon

Systems and methods for enticing users to access a web site