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 future, wearable computers are going to be exploited for different applications to support human life. In this paper, we present a new wearable computing system that supports daily conversation based on speech recognition and information retrieval technologies. In daily conversation, sometimes we do not know the meaning of a new word or we want to know more information about a topic. In such cases, we can get more information to make our conversation more interesting and fluent by receiving auxiliary information from a wearable computer. The proposed system analyzes conversation content and retrieves information related to ongoing conversations from the Internet to make them more interesting and beneficial. We have implemented a prototype system, and the results of actual use have shown the need for such a system.

An information retrieval system for supporting casual conversation in wearable computing environments

In a database broadcasting system, the server periodically broadcasts contents of database to mobile clients such as portable computers and PDAs. In this system, there are two possible methods for query processing: one is that a client stores all data which are necessary for processing a query and then processes it locally, and the other is that the server processes a query and broadcasts the query result to the query issue client. However, a client cannot get query results when the disk space of the client is not enough for query processing in the former method or when overfull queries are issued in the latter method. In this paper, to resolve these problems, we propose a method which processes a query by collaboration of the server and clients. In our method, the server adds identifiers to tuples appearing in the query result. Thus, the client can store only necessary tuples by referring to the identifiers. In addition, the server broadcasts rules which define the client’s behavior for receiving data, and then, the query results are constructed on the client by using the rules. In this way, clients can get query results efficiently.

A Collaborative Query Processing Method for a Database Broadcasting System.

There are many constraints on electrical power and communication in wireless sensor networks since sensor nodes can only be equipped with poor devices. Therefore, it is important to construct an effective communication route in terms of energy-efficiency and quality of communication. Many studies have been conducted to address this issue. In particular, controlling the transmission power of each individual node has been one of the hottest issues in recent years. Most conventional studies have assumed an environment in which radio signals transmitted by nodes propagate equally in all directions. However, this model cannot be applied to real environments due to several effects such as the presence of obstacles and antenna characteristics. In this paper, we propose a routing method based on the measured characteristics of radio propagation in a real environment. Our method first involves measuring the characteristics of radio propagation for all radio links. Second, the transmission power of each individual node and a communication route from the node to the sink node are determined based on the measured characteristics.

A Dynamic Route Construction Method Based on Measured Characteristics of Radio Propagation in Wireless Sensor Networks

On strategies for allocating replicas of mobile databases

As the performance measure for recursive query evaluation algorithms in the deductive database system, the largest amount of necessary storage space for intermediate results measured by the number of tuples has been usually employed. In this paper, using a practical performance evaluation measure based on the physical file access; namely, the number of pages accessed, we will investigate a general framework of efficient file access strategies for the transitive closure computation, which is one of performance bottlenecks in the recursive query evaluation. We introduce the page access graph for representing the page structure of a given file, and this graph provides the basis for considering the optimization of page access scheduling. Under the assumption that whole or a part of this graph is processed in the main memory, if the graph is acyclic, our algorithm provides the optimal page access scheduling, i.e., each page has to be fetched at most once.

Shojiro Nishio

Papers

An information retrieval system for supporting casual conversation in wearable computing environments

A Collaborative Query Processing Method for a Database Broadcasting System.

A Dynamic Route Construction Method Based on Measured Characteristics of Radio Propagation in Wireless Sensor Networks

On strategies for allocating replicas of mobile databases

File access level optimization using page access graph on recursive query evaluation