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

We propose a position detection mechanism enabling location-aware Pin&Play. Though the positions of inserted pins are important for most applications, it could not be detected in previous systems. The proposed method detects the positions of pins by processing images taken with a camera placed in front of the board. The proposed method has an error estimation mechanism to avoid the accumulation of position error. We also clarify the effectiveness of our proposed method through performance evaluations.

A Position Detection Mechanism enabling Location-aware Pin&Play

Satellite-based data broadcasting has been the focus of increasing attention in recent years due to launches of new broadcasting or communications satellites and the initiation of new services. The popularization of these kinds of broadcasting systems has led to a dramatic increase in both the broadcast volume and types of services. As a result, a system is needed that will enable an ordinary user to efficiently extract, store, and reuse required information from the massive amount of data that is broadcast. In response to this request, the authors propose a super active database system (SADB) and describe its design and implementation. The proposed SADB is an adaptation of an active database, which is an event-triggered database, to a broadcasting system. Since the SADB must quickly process the massive amount of data that is sent to it, the reception actions and reception data processing were designed independently of the rule processor. This paper evaluates the performance of an implemented system and shows the data volume that it can handle. © 2003 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 86(9): 87–99, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.10111

Design and implementation of an active database system for receiving broadcast data

The ability of the deductive database to handle recursive queries is one of its most useful features. It opens up new possibilities for users to view and analyze data. This ability to handle recursive queries, however, still cannot be fully utilized because when recursive rules are involved, the amount of deduced facts can become very large, making it difficult and sometimes impossible to store, view or analyze the query results. In order to overcome this problem, we have proposed the DSK method and the DSK(S) method to discover characteristic rules from large amount of deduction results without having to store all of them. In this paper, we propose two new methods, the DSK(T) method and the D-SK(ST) method which are faster than the DSK method and the DSK(S) method respectively. In addition, we propose a new sampling method called magic sampling, which is used by the two methods to achieve the improvement in speed. Magic sampling works when linear recursive rules are involved and the magic set algorithm is used for deduction.

Fast methods with magic sampling for knowledge discovery in deductive databases with large deduction results

In this paper, we propose an efficient node deployment strategy in sparse sensor networks. Our strategy, named DATFM/DF (Data Acquisition and Transmission with Fixed and Mobile node with deliberate Deployment of Fixed nodes), deploys sensor nodes based on the analysis of our previous mobile sensor control method, DATFM. As a result, DATFM/DF improves the efficiencies of sensing and data gathering. Furthermore, we conduct simulation experiments to evaluate the performance of our strategy.

An Efficient Node Deployment Strategy Strategy in Sparse Sensor Networks

In this paper, we consider a problem of range-based continuous query processing in mobile ad hoc networks (MANETs). Due to the advantage that no infrastructure is needed, MANETs are useful in various situations such as disaster sites. In such applications, information monitoring techniques are required, for example, to obtain the information on victims. In MANETs, the bandwidth and batteries of nodes are limited and nodes move freely, thus conventional techniques for fixed and wired networks are hard to apply to MANETs. Moreover, periodic data retrieval approach is inefficient because it incurs large traffic to obtain data items continuously. We therefore propose a communication-efficient method for continuously monitoring data existing around a query originating node. Our proposed method does not rely on periodic approach, but rather extends query transmission ranges dynamically and continuously to deal with nodes' movement. Query receiving nodes estimate the current position of the query originating node and forward data items toward the position. This approach also enables efficient data forwarding when data updates occur. Simulation experiments demonstrate the efficiency and scalability of our method.

Shojiro Nishio

Papers

A Position Detection Mechanism enabling Location-aware Pin&Play

Design and implementation of an active database system for receiving broadcast data

Fast methods with magic sampling for knowledge discovery in deductive databases with large deduction results

An Efficient Node Deployment Strategy Strategy in Sparse Sensor Networks

Range-based Continuous Threshold Query Processing in Mobile Ad Hoc Networks