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 this paper, we propose a novel data forwarding method for efficient data gathering in wireless sensor networks with multiple mobile sinks which freely move in the target region. In our proposed method, each sensor node recognizes its status of connection with mobile sinks based on the number of received beacons periodically emitted by each mobile sink. In addition, information on that status are shared among neighboring sensor nodes. Based on the shared information, each sensor node forwards data packets toward sensor nodes that frequently connect with mobile sinks. Furthermore, we verify the effectiveness of our proposed method through simulation experiment.

Data Forwarding Method Based on Status of Connection with Multiple Mobile Sinks in Wireless Sensor Networks

In this paper, we propose a new network topology discovery mechanism among ubiquitous chips, which are rule-based, event-driven input/output (I/O) control devices to compose ubiquitous computing environments. Since they achieve flexibility by describing behavior in a set of rules, we employ a rule-based approach to discover network topology. In ubiquitous computing environments, we use various communication methods and applications at the same time. Therefore, our flexible discovery mechanism works well in ubiquitous computing environments. Moreover, we verified our algorithm by implementing it on a topology discovery simulator and actual prototype devices of ubiquitous chips.

A rule-based discovery mechanism of network topology among ubiquitous chips

In this paper, we propose a consistency management method that constructs quorums with fewer nodes. In our method, assuming that the area where nodes exist is divided into several regions, each quorum is constructed based on the locations of the regions. As a result, the proposed methods can reduce the communication overhead while keeping the strict consistency among replicas.

Quorum-Based Consistency Management for Data Replication in Mobile Ad Hoc Networks

Recently, there has been increasing interest in research on data sharing in peer-to-peer networks. In our previous work, we proposed a novel update propagation strategy that creates an n-ary tree, whose root is the owner of the original data while the other nodes are peers holding its replicas, and propagates the update information according to the tree. In this paper, we extend our previous strategy to further reduce the delay for update propagation and to tolerate peers' failure. To achieve this, in the extended strategy, peers participating in the tree record the information of their ancestors and children in the tree and reconstruct the tree using that information when some peers in the tree fail.

An Update Propagation Strategy for Delay Reduction and Node Failure Tolerance in Peer-to-Peer Networks

Recently, the tremendous amount of information is digitalized and becoming available on the World Wide Web. These digital contents are nowadays easily accessible to a lot of Internet users. However, most web sites today are comprised of a large number of web pages and hyperlinks, often making it difficult for visitors to browse through and to find their desired information quickly. To address the problem, this paper proposes a newly web site personalization method by utilizing content browsing history. Web sites based on our model consist of a set of small components, and browsing history considered as visitor's preference is used to identify components to be presented on a web page.

Shojiro Nishio

Papers

Data Forwarding Method Based on Status of Connection with Multiple Mobile Sinks in Wireless Sensor Networks

A rule-based discovery mechanism of network topology among ubiquitous chips

Quorum-Based Consistency Management for Data Replication in Mobile Ad Hoc Networks

An Update Propagation Strategy for Delay Reduction and Node Failure Tolerance in Peer-to-Peer Networks

Realization of personalized presentation for digital contents based on browsing history