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

Ja-Net on Grid は,動的に変化するグリッド環境における並列計算を容易にするためのモバイル エージェントシステムである.本システムではこれまで Globus Toolkitのセキュリティ基盤 GSIを 用いて,信頼できるエージェントのみをホストが受け入れられるようにセキュリティを構築してきた. しかし,エージェント側のセキュリティは考慮していなかったため,悪意のあるホストにエージェント の内部変数を不正に書き換えられるなどエージェントがホストに攻撃される可能性がある.信頼でき ないホストへのエージェントの移動を禁止することで,エージェントのセキュリティを保つことが可 能であるが,利用できるホストが減少してしまうため,グリッド環境で提供されている膨大な計算リ ソースを最大限に活用することができない.そこで本研究では,たとえ信頼できないホスト上であっ ても,モバイルエージェントが一定のセキュリティを確保し,その結果グリッド上のすべてのホスト で処理を継続できる手法を提案する.本論文では,提案手法の詳細とプロトタイプの実装およびその パフォーマンス評価について述べる.

A Migration Method of Mobile Agents on Ja-Net on Grid for Increasing Reliability of Host Utilization

The tree-type algorithm has been proposed and investigated widely as a random multiple-access communication protocol. It is a distributed algorithm but shows a stable throughput performance. Several proposals have been made, including Tsybakov's, for the tree-type algorithm with a reservation mechanism to improve further the throughput performance.



This paper proposes new TREE-DTA with reservation mechanism, which is based on the block access method and employs the deterministic Q-ary tree-type algorithm operating on the terminal addresses as the retransmission algorithm. The feature of the reservation mechanism proposed in this paper is that the realization is easy, compared to various other methods that have been proposed.



To evaluate the performance of TREE-DTA with reservation mechanism, a mathematical model is used to derive the approximate expression for the relation between the throughput and the average delay. Furthermore, the performances of TREE-DTA with reservation mechanism are examined by a simulation experiment. The robustness to the system error and the application of the priority scheme are discussed.

Deterministic tree‐type algorithm with reservation mechanism

A Method for Context Recognition Using Peak Values of Sensors

The conventional approaches for constructing real-space-based 3D space on the WWW are very costly in initial modeling and in network access in its operation. The image based rendering (IBR) approach focuses on more convenient handling of the real space, but the obtained space is usually constrained by the difficulty in image analysis. As a result, the number of people who use virtual space on the WWW is still limited. In order to relax the modeling cost and the accessing cost through the WWW to let everyone enjoy easy construction and easy browsing of real and attractive virtual space, the authors propose a simple approach where they use scenic images such as those taken by a digital camera without distorting them. In the system, users can browse and construct real and attractive virtual space on the WWW more easily than ever.

W@nderland: image-based constructable virtual space on the WWW

With the popularization of today's mobile devices such as PDAs, cellular phones, and laptop PCs, the environ- ment that enables us to publish and utilize information via the Internet has been actualized. In the near future, other kinds of devices such as network-enabled appliances and sensors will also be connected to the Internet. Users will be able to get various kinds of information such as their surrounding contexts like location and weather from other users via the Internet, and many context-sensitive services that utilize these information will be developed. The context-sensitive services often require retrieval of location-based information because context information is often location-dependent. We consider P2P networking to be promising in the environment mentioned above, but the existing flooding-based retrieval algorithms commonly have a problem that they generate a huge amount of network traffic. To cope with this problem, we propose a P2P overlay network construction method based on device locations to realize efficient retrieval of location-dependent information.

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Shojiro Nishio

Papers

A Migration Method of Mobile Agents on Ja-Net on Grid for Increasing Reliability of Host Utilization

Deterministic tree‐type algorithm with reservation mechanism

A Method for Context Recognition Using Peak Values of Sensors

W@nderland: image-based constructable virtual space on the WWW

LL-Net: Location-based Logical P2P Network for Mobile Environment