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 ad hoc networks, data accessibility decreases due to network divisions. To solve this problem, it is effective that each mobile host creates replicas of data items held by others. In this paper, we assume an environment where mobility and access characteristics of mobile hosts have the locality and propose a method that locally relocates replicas just before a network division occurs.

A replica allocation method adapting to topology changes in ad hoc networks

In this paper, we propose new cooperative caching strategies by multiple clients in a push-based broadcast system, which replaces cached items based on benefits of the waiting time. A key idea is that the clients construct a logical peer-to-peer network and each of them determines the replacement of its own cache by taking into account access probabilities to data items from neighboring clients and the broadcast periods of data items or the times remaining until these items are broadcasted next. We confirm that the proposed strategies reduce the average response time by simulation experiments.

Cooperative caching by clients constructing a peer-to-peer network for push-based broadcast

In this paper, we discuss several strategies for efficient processing of three types of queries concerning mobile hosts; (1) queries to obtain the location of a mobile host (location queries), (2) queries to determine whether the mobile host is currently active (existence queries), and (3) queries to obtain a piece of information from a mobile host (data queries). Extracting five fundamental strategies from those usually employed in mobile communication protocols, we shall discuss their features and then compare their performance. These five strategies are: single broadcast notification (SBN), double broadcast notification (WBN), broadcast query forwarding (BQF), single default notification (SDN), and double default notification (WDN). We show that the optimal strategy varies according to network conditions such as network topology, network scale, migration rate, and query occurrence rate.

Strategies for Query Processing in Mobile Computing

In this paper, based on the Hybrid Wireless Broadcast (HWB) model, we investigate a cooperative data management strategy to provide more efficient data delivery to the mobile clients. This strategy integrates broadcast scheduling with cache management of the base station by making effective use of the HWB data dissemination, namely push- and pull- based broadcast and pull-based point-topoint wireless communication. Simulation study demonstrates that the proposed cooperation strategy can improve the system performance.

On a Cooperation of Broadcast Scheduling and Base Station Caching in the Hybrid Wireless Broadcast Environment

People routinely carry mobile devices in their daily lives and obtain a variety of information from the Internet in many different situations. In searching for information (content) with a mobile device, a user's activity (e.g., moving or stationary) and context (e.g., commuting in the morning or going downtown in the evening) often change, and such changes can affect the user's degree of concentration on his or her mobile device's display and information needs. Therefore, a search system should provide the user with an amount of information suitable for the current activity and a type of information suitable for the current context. In this study, we present the design and implementation of a content search system that considers a mobile user's activity and context, with the goal of reducing the user's operation load for content search. The proposed system switches between two kinds of content search systems according to the user's activity: the location-based content search system is activated when the user is stationary (e.g., standing and sitting), while a menu-based content search system is activated when the user is moving (e.g., walking). Both systems present information according to user context. The location-based system presents detailed information via menus and a map according to location-based categories. The menu-based system presents only a few options to enable users to get content easily. Through user experiments, we confirmed that participants could get desired information more easily with this system than with a commercial search system.

Shojiro Nishio

Papers

A replica allocation method adapting to topology changes in ad hoc networks

Cooperative caching by clients constructing a peer-to-peer network for push-based broadcast

Strategies for Query Processing in Mobile Computing

On a Cooperation of Broadcast Scheduling and Base Station Caching in the Hybrid Wireless Broadcast Environment

A content search system considering the activity and context of a mobile user