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 method for selecting appropriate data based on personal information of mobile users to improve the success rate for requesting data in a ubiquitous computing environment. With this method, the output data is selected based on users' requirements. Then, a ubiquitous terminal that actually outputs the selected data is selected based on the facility and activity level of all terminals and the category of the selected data. As a result, our method can output effective data for mobile users in a ubiquitous computing environment where there are many types of data and ubiquitous terminals. Furthermore, we show the effectiveness of our method by a simulation experiment.

A method for selecting output data from ubiquitous terminals in a ubiquitous computing environment

The availability of machine readable taxonomy has been demonstrated by various applications such as document classification and information retrieval. One of the main topics of automated taxonomy extraction research is Web mining based statistical NLP and a significant number of researches have been conducted. However, existing works on automatic dictionary building have accuracy problems due to the technical limitation of statistical NLP (Natural Language Processing) and noise data on the WWW. To solve these problems, in this work, we focus on mining Wikipedia, a large scale Web encyclopedia. Wikipedia has high-quality and huge-scale articles and a category system because many users in the world have edited and refined these articles and category system daily. Using Wikipedia, the decrease of accuracy deriving from NLP can be avoided. However, affiliation relations cannot be extracted by simply descending the category system automatically since the category system in Wikipedia is not in a tree structure but a network structure. We propose concept vectorization methods which are applicable to the category network structured in Wikipedia.

Concept vector extraction from Wikipedia category network

Due to the recent popularization of mobile multimedia broadcasting, broadcasting continuous media data such as audio and video has attracted great deal of attention. In general continuous media data broadcasting, since clients have to wait to receive data before playing it, various schemes to reduce waiting time have been studied. Some reduce the waiting time by dividing the data into several segments and broadcasting preceding segments frequently with a single channel. However, by dividing the data into numerous segments and producing an effective broadcast schedule, the waiting time can be further reduced. In this paper, we propose a scheduling protocol to reduce waiting time with large-scale data segmentation.

A Scheduling Protocol for Continuous Media Data Broadcasting With Large-Scale Data Segmentation

Recently, there has been an increasing interest in a social network. In a social network, nodes and links represent participants and their friendships, respectively. We have designed and implemented a query propagation mechanism and its applications to realize a social network composed by cellular phone users. In these applications, users can retrieve information from their friends or their friends friends by propagating the query in the network. To propagate a query in a wide range and improve the query success ratio, most users who receive the query must relay it to all their friends. However, this increases communication packets. In this paper, we propose a query routing method to decrease the number of communication packets by using user profiles.

Shojiro Nishio

Papers

A method for selecting output data from ubiquitous terminals in a ubiquitous computing environment

Concept vector extraction from Wikipedia category network

A Scheduling Protocol for Continuous Media Data Broadcasting With Large-Scale Data Segmentation

Profile-based Query Routing in a Mobile Social Network

Autonomous mobile bionanosensor networks for target tracking: A two-dimensional model