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

Today, billions of users use the Internet to access multimedia information on the World Wide Web (WWW). Most users, however, are given low-speed connection to the Internet and are often irritated by the long transmission time of WWW pages. Therefore, it is very important for information providers to tailor the presentation of information to a user's network environment. In this paper, we propose an architecture for a WWW server with a media scaling mechanism. The WWW server can control the transmission time of WWW pages based on the transmission time threshold specified by the information providers. We also describe our implementation of the WWW server and discuss some experimental results.

A WWW server with media scaling mechanism based on page transmission latency

In this paper, we propose a route planning method for mobile sensor nodes using cost map. The proposed method achieves novel route planning that can solve several practical problems from previous works: the limitations of sensing ranges, obstacles on the node's routes, and restriction of their movements. This method can determine the route that has the lowest power consumption. We also propose a sensing area defining method for dealing with many kinds of sensors. Furthermore, we compared the proposed method to A*algorithm, a well-known route planning algorithm. We implemented a prototype of the sensor node to verify our algorithm in a real environment.

A Route Planning Method Using Cost Map for Mobile Sensor Nodes

In mobile ad hoc networks (MANETs), to acquire only necessary data items, it is effective that each mobile node retrieves data items using a top-k query, in which data items are ordered by the score of a particular attribute and the query-issuing mobile node acquires data items with k highest scores. In our previous works, we proposed top-k query processing methods for reducing traffic and also keeping high accuracy of query result. Those methods focus on the traffic reduction by not sending back unnecessary data items. However, queries are forwarded to all of nodes in the entire area by flooding, and thus, many nodes which do not contribute to the query result have to send redundant top-k queries and replies, which increases unnecessary traffic. In this paper, we propose a routing method for top-k query processing in MANETs. In this method, when each mobile node sends a top-k query, it refers to its own routing table consisting of the ranking of scores of data items in the network and identifiers of nodes to which queries are forwarded to obtain the necessary data items. Moreover, each node updates its own routing table when the link disconnection occurs and scores of data items are updated.

A Routing Method for Top-k Query Processing in Mobile Ad Hoc Networks

With the widespread use of satellite broadcasting and data broadcasting, the kind and amount of broadcast content are increasing rapidly. In an environment in which large amounts of data are received, it is very difficult for the user to locate the necessary data, and it is desirable to develop a filtering method that can automatically extract only the necessary data. A large number of filtering methods for such a purpose have been proposed. However, the mathematical basis for the qualitative representation of these methods has not been established, and there is as yet no method for qualitative evaluation and optimization of the method and no design of a declarative language for filtering. In this paper, filtering is defined as a function. By representing the properties of various filtering methods as conditions to be satisfied by the function, a mathematical basis for information filtering is established. The relations among the properties of the filtering methods are analyzed by identifying the inclusive relations among the constraints representing various properties. On the basis of the mathematical basis discussed in this paper, real filtering methods are classified. By selecting processing matched to the required properties, more efficient filtering will be realized. © 2003 Wiley Periodicals, Inc. Electron Comm Jpn Pt 2, 86(11): 37–49, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjb.10199

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On functional properties of information filtering

Map-based application is one of the most popular applications using a built-in GPS receiver. Users often use applications based on geographic information in situations where they share the same information needs with friends or families and conduct some tasks together. However, it is difficult to communicate geographic information with each other because existing map-based applications are basically designed for solitary work. In this paper, to support collaborative work using map-based applications, we explore important factors for collaborative use of map interface. We conducted a user experiment using a map-based application implemented with some functions for supporting collaborative work. The result shows that it is important to i) enable users to share information among multiple devices and view it individually on their own displays when they use a map together, and ii) synchronize users' displays and map operations when they communicate geographic information with each other.

Shojiro Nishio

Papers

A WWW server with media scaling mechanism based on page transmission latency

A Route Planning Method Using Cost Map for Mobile Sensor Nodes

A Routing Method for Top-k Query Processing in Mobile Ad Hoc Networks

On functional properties of information filtering

Exploring map-based interactions for co-located collaborative work by multiple mobile users