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 the Skip Graph, which is a structured overlay network that supports range retrievals, a key is supposed to consist of a single value. Therefore, the Skip Graph cannot perform range-to-range retrievals. In the present research, we extend the Skip Graph, enabling it to retain a range as a key, and propose the Range-Key Skip Graph, which can perform range-to-range retrievals. We also implement the proposed scheme and evaluate it on the PlanetLab.

Range-Key Extension of the Skip Graph

Mobile internet access has become an important technology to our life. According to this trend, search engines have been providing search services for mobile phones. Since a mobile phone has a limited interface, such as a small screen and keypad, even simple interactions are burdensome for users. Therefore, to provide comfortable web browsing experience, efforts on HCI aspect are also essential. In this paper, we describe our studies that tackle this problem by HCI-based approach. We propose systems that reduce the number of operations on inputting a query to a search engine, enable clipping and saving a web content, and assist cooperative web search conducted by multiple mobile users.

Mobile search assistance from HCI aspect

In this paper, we describe in silico experiments and information theoretic analysis of mobile bionanosensor networks for target detection and tracking. A mobile bionanosensor network assumed in this paper consists of bio-nanomachines that coordinate their movement through molecular communication. We use a biologically realistic model of chemotactic bacteria to design a bacterium-based bionanosensor network. We then perform in silico experiments to demonstrate that chemotactic bacteria are promising materials to implement bionanosensor networks for target detection and tracking. Further, we show how information theory may apply to evaluate the performance of bio-nanomachines that form a mobile bionanosensor network.

In Silico Experiments of Mobile Bionanosensor Networks for Target Tracking

Despite the drop in prices, storage cost is still a major cost factor in large scale database applications, such as data warehouses. Data compression is needed to reduce the cost. Many data compression techniques have been proposed and the issue of database compression has been discussed. Conventional data compression techniques require that compressed data be decompressed before read operations or write operations can be carried out. As a result, it is not practical to compress databases in active use using the conventional data compression techniques. In this chapter, we propose a database compression technique which needs only partial decompression for read operations and no decompression for write operations. It is suitable for databases in active use and can be used to compress data in relational databases. The proposed technique finds rules in a relational database using the Apriori Algorithm and store data using rules to achieve high compression ratios. The rules are in turn stored in a deductive database to enable easy data access.

Database Compression with Data Mining Methods.

In this paper, we propose a novel sensor data collection method that forwards messages to the nodes which are suitable for reconstructing of the sensor data distribution by extending hierarchical Delaunay overlay network (HDOV). By our proposal method, contour lines map can be reconstructed according to the requirements, satisfying a specified system constraint of the number of sensor data. The key idea of the method is allocation of the number of sensor data to be collected according to the layered structure of HDOV. The simulation result shows that our proposal can reconstruct contour lines map which focuses on the characteristic point or not, satisfying the specified system constraint.

Shojiro Nishio

Papers

Range-Key Extension of the Skip Graph

Mobile search assistance from HCI aspect

In Silico Experiments of Mobile Bionanosensor Networks for Target Tracking

Database Compression with Data Mining Methods.

A sensor data collection method under a system constraint using hierarchical Delaunay overlay network