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

On Transaction Management for Mobile Clients and Servers

Skyline query processing reveals a set of preferable results based on the competitiveness of many criteria among all data objects. This is a very useful query for multi-attribute decision making. Moreover, monitoring and tracing skyline over time-series data are also important not only for real-time applications (e.g., environmental monitoring) but also historical time-series analysis (e.g., sports archives, historical stock data). In these applications, considering consecutive snapshots, a large fraction of the fixed number of observing objects (e.g., weather stations) can change their values resulting to the possibility of complete change in the previous skyline. Without any technique, computing skyline from a scratch is unavoidable and can be outperformed some traditional skyline update methods. In this paper, we propose an efficient method to compute skyline sets over data update streams. Our proposed method uses bounding boxes to summarize consecutive data updates of each data object. This technique enables the pruning capability to identify a smaller set of candidates in skyline computation resulting in faster total computation time. We conduct some experiments through both synthetic and real-life datasets. The results explicitly show that our proposed method significantly runs faster than the baseline in various parameter studies.

Candidate Pruning Technique for Skyline Computation Over Frequent Update Streams

In this paper, we propose an energy-efficient data gathering method named ODAS/SS (Overhearing based Data Aggregation using Spatial interpolation with Sleep Scheduling) in Wireless Sensor Networks. ODAS/SS reduces the data traffic using spatial interpolation and overhearing, which is a typical characteristic of wireless communications. In addition, ODAS/SS applies a sleep scheduling mechanism, which lets some nodes transit to the sleep mode while guaranteeing the coverage in the target region, in order to further reduce the energy consumption and suppress the variation in residual energy among nodes. Furthermore, we present the results of simulation experiments to verify the effectiveness of ODAS/SS.

Energy-Efficient Data Gathering Using Sleep Scheduling and Spatial Interpolation in Wireless Sensor Networks

In this paper, we propose a web contents sharing system which allows a user to save web contents presented on internet and share them on P2P (peer to peer) network. In this system, a user can use the URL, the last update date and time, the size or some other information in order to search web pages. In addition, a user can get web pages that have existed and do not currently exist if they are stored in other systems. Furthermore, we realized a mechanism for authors to control contents delivery, such as the prohibited of forwarding the contents and notification to the authors on forwarding contents. Keyword Peer-to Peer, Web, Date Sharing, Contents Delivery, Delivery Control

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A web contents sharing system with contents delivery control functions

Cellular phones are widely used to access the Web, and it has become common to search for information on the Web using cellular phones. However, since cellular phones only have numerical keys for inputting characters, users have to conduct laborious operations to input a keyword for Web search. In our previous work, we assumed information appliances equipped with pointing devices and proposed a Click-Search method that enables users to perform a keyword search simply by pointing and clicking the target word on a Web page. This approach might be effective for Web searches using cellular phones. However, since many cellular phones only have a, direction pad as a pointing device, users still have to conduct laborious operations using the pad to move a pointer and point to a word. To solve this problem, we propose a Click-Search interface for cellular phone users that uses an expanding circle to select a word for search. Using this interface, users can specify a keyword simply by pushing a few button.

Shojiro Nishio

Papers

On Transaction Management for Mobile Clients and Servers

Candidate Pruning Technique for Skyline Computation Over Frequent Update Streams

Energy-Efficient Data Gathering Using Sleep Scheduling and Spatial Interpolation in Wireless Sensor Networks

A web contents sharing system with contents delivery control functions

A click-search interface for web browsing using cellular phones