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

A Prefetching Method Considering Mobility of Avatars in a Broadcast-based Cyberspace

In a kNN query processing method, it is important to appropriately estimate the range that includes kNNs. While the range could be estimated based on the node density in the entire network, it is not always appropriate because the density of nodes in the network is not uniform. In this paper, we propose two kNN query processing methods in MANETs where the density of nodes is ununiform; the One-Hop (OH) method and the Query Log (QL) method. In the OH method, the nearest node from the point specified by the query acquires its neighbors' location and then determines the size of a circle region (the estimated kNN circle) which includes kNNs with high probability. In the QL method, a node which relays a reply of a kNN query stores the information on the query result for future queries.

Searching K-nearest neighbor nodes based on node density in ad hoc networks

Recent advance in ubiquitous technology has led the development of ubiquitous sensor environment in which various kinds of sensor devices provide sensor data. In this environment, since an enormous number of sensor devices join the network, it is effective to construct a P2P network for efficient retrieval of sensor data. In general, each sensor device periodically performs sensing based on the cycle set by itself. On the other hand, users often issue queries to acquire sensor data that were sensed at a specific time. In such a case, only the sensor devices (peers) that perform sensing at the specified time have to return their sensor readings. In this paper, we propose a novel P2P network construction method to achieve effective sensor data collection considering queries that specify a certain time. Our method clusters peers which perform sensing simultaneously, and constructs logical links between clusters whose peers sometimes perform sensing at a same time.

A P2P Network Construction Method Based on Cycle of Sensing in Ubiquitous Sensor Environment

Location-based service (LBS) is a typical application for mobile ad hoc networks (MANETs). In an LBS, it is effective for nodes to acquire data using convex hull queries, which retrieve the information necessary for calculating the convex hull of all nodes composing a given network. In a naive approach, the query-issuing node can determine the convex hull of the network by flooding a query throughout the entire network and receiving location information on all the nodes, but this involves excessive traffic. In addition, since the network topology changes dynamically, due to the movement of nodes, the set of vertices of the convex hull changes. Therefore, the query-issuing node must determine the convex hull in a short period of time. In this paper, we propose convex hull query processing methods for reducing traffic and response time while maintaining high accuracy of the query result in MANETs. In these methods, nodes reply with information on nodes which are convex hull vertices. In the Local Convex Hull (LCH) method, nodes reply with information on nodes which are vertices of the local convex hull, which is the convex hull composed of nodes whose information has already been received. In the Local Wrapping (LW) method, the query is transmitted only to nodes on the outer boundary. Experimental results show that our proposed methods reduce traffic and achieve short response time and high accuracy of the query result, in comparison with a naive method.

Processing Convex Hull Queries in MANETs

In broadband networks, appropriate use of database migration can drastically shorten the average transaction processing time of a distributed database system. In previous work, we have proposed a transaction processing method based on database migration, in which we assumed that the network bandwidth is very broad and almost uniform over the whole network. However, WANs consist of various types of networks, including private LANs, narrowband public networks and broadband backbones; thus, that method cannot be applied migration scheduling method which gives that shortest communication time for database operations, and is suitable for use in WANs. We also prove that the proposed method can find the database migration schedule which gives the shortest communication time for a given access sequence.

Shojiro Nishio

Papers

A Prefetching Method Considering Mobility of Avatars in a Broadcast-based Cyberspace

Searching K-nearest neighbor nodes based on node density in ad hoc networks

A P2P Network Construction Method Based on Cycle of Sensing in Ubiquitous Sensor Environment

Processing Convex Hull Queries in MANETs

Database Migration in WAN Environments: How Can It Earn Good Performance?