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

In this paper, we propose an energy-efficient data gathering method, which is an extension of our previous method, to further reduce traffic in wireless sensor networks. Our previous method suppresses the energy consumption by traffic reduction utilizing spatial and temporal interpolation. In the extended method, each node appropriately utilizes either of spatial or temporal interpolation according to the change in spatial and temporal characteristics of data distribution and requirements specified by the application. Furthermore, we verify the effectiveness of the extended method by a simulation experiment.

Energy-Efficient Data Gathering Using Traffic Reduction Based on Change in Data Characteristics in Wireless Sensor Networks

In recent years, it has become popular to use multimedia data for building presentations. In cases of real-time contents are built, a powerful mechanism is necessary for extracting materials suited to the situation from a large amount of multimedia data. In this study, as an example of such a mechanism, we constructed the Active Karaoke system, which generates background scenes for karaoke using an active database. An active database is a database system that processes prescribed actions automatically in response to the occurrence of an event. We show that Active Karaoke can extract appropriate multimedia data from the database in response to the change of music tones and contents of lyrics, and display them as background scenes. Since an active database is used for storing and managing data, the system behavior can be easily and flexibly described using ECA rules, which are the behavior description language of an active database.

A System for Generating Background Scenes of Karaoke Using an Active Database System

Recently, peer-to-peer (P2P) networks are becoming popular as communication forms for ubiquitous environments. Flooding-based P2P networks such as Gnutella have a problem that they generate a huge amount of network traffic. Moreover, their recall ratio is generally low because their topologies are constructed regardless of preference of each peer. In this paper, we propose a topology re-formation algorithm for flooding-based P2P networks. In the proposed algorithm, each peer observes responses that passed through itself and re-forms the topology so that peers with a content matching the query can be located near the query issuer. By simulation experiments, we show that peers with similar preferences get collected in the P2P network, and the recall ratio improves greatly

Topology Re-formation Algorithms for Ubiquitous P2P Networks Based on Response Statistics

Recently, due to remarkable advancements of computer technology, multiple applications can be simultaneously run on a single computer. These applications commonly use a mouse as an input device. However, with a single mouse, we can only input two-dimensional coordinates and press mouse buttons for input. Therefore, this conventional input method using a single mouse handled by one hand is not sufficient to support complex or parallel operations in sophisticated applications. On the other hand, it is widely recognized that using two different input devices handled by two hands is useful for complex operations. In this paper, we show our design and implementation of a multipurpose interface which integrates two mice as input device for window system.

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Shojiro Nishio

Papers

Energy-Efficient Data Gathering Using Traffic Reduction Based on Change in Data Characteristics in Wireless Sensor Networks

A System for Generating Background Scenes of Karaoke Using an Active Database System

Topology Re-formation Algorithms for Ubiquitous P2P Networks Based on Response Statistics

Design and implementation of the double mouse system for a Window environment

Design and implementation of a news on-demand system integrating retrieval and editing of news contents