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

Because of the recent development of radio frequency identification (RFID) technologies, various systems for RFID tags have been proposed. Since RFID tags only have a simple function, i.e., sending data, they can be available for various purposes. Accordingly, by customizing RFID tag systems, we can expand their applications. However, previous systems have been usually proposed for one special purpose only. In this paper, we propose a rule-based RFID tag system using ubiquitous chips. Our previously proposed ubiquitous chips are rule-based I/O control devices. By applying rule-based principles, we can easily customize the RFID tag system and can construct flexible, scalable, and easy exploitable systems.

A rule-based RFID tag system using ubiquitous chips

In order to utilize the real world via computers by means of camera images in augmented reality systems, the concise coordinate information of the systems must be identified. Image-based approaches are promising for this purpose since they provide direct mapping from the camera images to the coordinate system for virtual objects, and therefore, no camera calibration is necessary. However, the conventional image-based markers are typically stickers, and the information they deliver is limited and susceptible to change dynamically. In this paper, we propose a location marking method, called VCC (visual computer communication), which uses more intelligent markers. In our method, a marker, consisting of 16 LEDs (light-emitting diodes), keeps blinking to provide both coordinate information and attached information such as an address or a URL.

Realizing a visual marker using LEDs for wearable computing environment

This paper describes an e-mail system for wearable computing environments. In this system, we extend the conventional mail format and the server/client(browser) architecture by considering the specific features of wearable computing environments, i.e., full time operation, hands-free use of computer, and close relationship to our daily life. A mail author can specify the behavior of his/her mail by embedding several useful commands in the mail. A user can specify in the mail various conditions as commands such as time, location of the recipient, and status of various sensors to allow an adaptive behavior in the mail. We also describe other features of the system to support user's wearable computing life.

W-mail: an electronic mail system for wearable computing environments

In mobile wireless sensor networks (MWSNs), since sensor nodes are highly resource constrained, it is effective to retrieve sensor observations using a top-k query, in which the observations are ordered by the value (or score) of a particular type of sensor, and the query-issuing node acquires sensor observations with the $k$ highest scores. In our previous work, we proposed a message processing method for a top-k query in mobile ad hoc networks (MANETs) for reducing traffic and keeping the accuracy of the query result. This method basically works well, however, it cannot avoid the deterioration of the accuracy when network partitioning occurs. In this paper, we assume MWSNs and propose data replication methods for keeping high accuracy of the query result even at the point of network partitioning. The proposed methods replicate sensor observations with high scores when each node sends and relays sensor observations to the query-issuing node.

Data Replication for Top-k Query Processing in Mobile Wireless Sensor Networks

In ad hoc networks, it is effective that each mobile host creates replicas of data items held by other mobile hosts for improving data accessibility. When a mobile host requests a data item (replica) held by another mobile host, it usually uses the shortest path for the data transmission. In this case, mobile hosts at the center of the network relay many data items and consume much power. In this paper, we propose a data access method to balance the power consumption among mobile hosts. This method selects a path between the request issuing host and the requested data holder considering the path length and the remaining amount of batteries of mobile hosts along the path. We present simulation results to evaluate the performance of our proposed method.

Shojiro Nishio

Papers

A rule-based RFID tag system using ubiquitous chips

Realizing a visual marker using LEDs for wearable computing environment

W-mail: an electronic mail system for wearable computing environments

Data Replication for Top-k Query Processing in Mobile Wireless Sensor Networks

A data access method considering power consumption in ad hoc networks