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.

We present the Stanford Question Answering Dataset (SQuAD), a new reading comprehension dataset consisting of 100,000+ questions posed by crowdworkers on a set of Wikipedia articles, where the answer to each question is a segment of text from the corresponding reading passage. We analyze the dataset to understand the types of reasoning required to answer the questions, leaning heavily on dependency and constituency trees. We build a strong logistic regression model, which achieves an F1 score of 51.0%, a significant improvement over a simple baseline (20%). However, human performance (86.8%) is much higher, indicating that the dataset presents a good challenge problem for future research. 
The dataset is freely available at this https URL

SQuAD: 100,000+ Questions for Machine Comprehension of Text

Due to the recent popularization of digital hroad- casting systems, broadcasting continuous media data such as music or movies has attracted much attention. In general continuous media data broadcasting, since clients have to wait to receive data before playing it, various methods to reduce waiting time have been studied. Some reduce the waiting time by dividing the data into several segments and broadcasting precedent segments frequently with a single channel. However, by dividing the data into finer segments and producing an effective broadcast schedule, the waiting time can he further reduced. In this paper, we propose an effective scheduling method to reduce waiting time with finer data division.

http://ieeexplore.ieee.org/iel5/9617/30391/01401688.pdf

An Effective Scheduling Method with Finer Data

As the recent advances in the wireless technologies and mobile terminals, mobile users equipped with mobile devices are able to access wireless services through 3G cellular network, WiFi hotspot, or WiMAX link, as well as through satellite digital broadcast or terrestrial digital broadcast. By effectively taking advantage of these complementary communication modes, we explore a new hybrid data delivery model, i.e., Hybrid Wireless Broadcast (HWB) model to benefit from the optimal combination of the push-based and pull-based broadcast and on-demand point-to-point wireless communication. The HWB model can provide a flexible and complementary information service in different bandwidths and service ranges, and greatly improve the responsibility, scalability, and efficiency of the system. The results of simulation study show the proposed HWB approach achieves a significant improvement in system performance.

A Hybrid Data Delivery Method of Data Broadcasting and On-demand Wireless Communication

Due to the recent development of the Internet, broadcast and communication integrated video-on-demand services have attracted great attention. Playback interruptions occur during playing videos when the Internet bandwidth is insufficient for the video delivery. Hence, various methods to reduce the interruption time have been proposed. However, they do not consider the psychological interruption time which changes according to the attention to interruptions or the concentration to video playbacks. Therefore, the users' motivations for playing videos can be depressed largely in the existing methods. In this paper, we discuss an interruption control for video-on-demand systems. Also, we extended previous methods to control interruptions and evaluated their performances for interruptions.

Evaluations on Playback Interruption Control for Broadcast and Communication Integrated Video-on-Demand Systems

To deliver web pages effectively and efficiently, many techniques has been developed. Among them, a web page transmission mechanism which can control the transmission order of inline objects has been proposed. It employs a new transfer protocol, HTSP (Hypertext Streaming Protocol), in which a client requests one or more inline objects by a single request along with a transmission order specification. However, the behavior of caching proxies has not been considered in the protocol design. In this paper, we consider how a caching proxy should behave in the transmission order control mechanism, and then extend the HTSP to realize that behavior. We analyze the potential benefit of the extended HTSP, and verify it by performance evaluation in an experimental environment.

Extending hypertext streaming protocol to realize effective web page transmission via a caching proxy

Recently, hybrid broadcasting systems have attracted great attention. In video delivery on hybrid broadcasting systems, clients can receive data both from communication systems and broadcasting systems. In conventional methods, the clients that have much data receive data from communication systems. This lengthens the data reception time for the clients that do not have much data and causes longer interruption times. In this paper, we propose data reception methods in that only the clients that are playing the former parts of the video receive the data from communication systems. Since clients that do not have much data can receive the former parts faster and the latter parts from the broadcast systems, their interruption times can be reduced effectively.

Shojiro Nishio

Papers

An Effective Scheduling Method with Finer Data

A Hybrid Data Delivery Method of Data Broadcasting and On-demand Wireless Communication

Evaluations on Playback Interruption Control for Broadcast and Communication Integrated Video-on-Demand Systems

Extending hypertext streaming protocol to realize effective web page transmission via a caching proxy

Video Data Reception Methods Considering Playing Position on Hybrid Broadcasting Environments