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

Sleep scheduling algorithms for duty-cycled sensor networks have received great attention, since sensors should dynamically be awake and asleep to save energy consumption. Among all current sleep scheduling algorithms, only the recently proposed energy consumed uniformly-connected k-neighborhood (EC-CKN) algorithm focuses on not only the coverage of the resultant network, but also the network lifetime after sleep scheduling. However, all sleep scheduling processes ignore the fact that potential insider attacks actually can seriously affect or destroy proper sleep scheduling operations and properties. In this paper, we discuss these vulnerabilities and propose corresponding countermeasures for EC-CKN, i.e., secure neighborhood authentication.

Secured energy-aware sleep scheduling algorithm in duty-cycled sensor networks

Recently, many broadcast satellites have been launched to provide data broadcasting services for public users. Although the provided services can cover many kinds of data and a wide range of user interest, it is considered that, in general, a user is interested in only some specific genres of data. Consequently, storing all received data is considered to be inefficient and only wasting a large amount of memory. This motivated us to introduce an information filtering system into a broadcast data receiving system. The use of filtering system increases the efficiency of memory usage and reduces time for searching interesting data. In this paper, we propose a filtering method that uses a tree structure to represent user preferences. The use of this filtering method enable the receiving system to select only data that match user's interest and classify the stored data in the way that suits the user's access pattern. We also describe the design and implementation of our broadcast data. Receiving system that makes use of the proposed filtering method. Further, we evaluate the performance of our method by showing some simulation results.

A user customized selection and categorization for broadcast data

A lot of bilingual dictionaries have been released on the WWW. However, these dictionaries insufficiently cover new and domain-specific terminology. In our demonstration, we present a dictionary constructed by analyzing the link structure of Wikipedia, a huge scale encyclopedia containing a large amount of links between articles in different languages. We analyzed not only these interlanguage links but extracted even more translation candidates from redirect page and link text information. In an experiment, we already proved the advantages of our dictionary compared to manually created dictionaries as well as to extracting bilingual terminology from parallel corpora.

A bilingual dictionary extracted from the Wikipedia link structure

Two algorithms, control token transfer algorithm (CTTA) and regenerate, are proposed to realize mutual exclusion in a computer network environment using a resilient circulating token. The algorithm CTTA uses a message called token to transfer the privilege of entering a critical region among the participating sites. The algorithm regenerate checks whether the token is lost during system failures, and regenerates it if necessary. Failures in a computer network are classified into three types. The execution of these two algorithms is described for each type of system failure. To detect system failures, a time-out mechanism based on message delay is used. >

A time-out based resilient token transfer algorithm for mutual exclusion in computer networks

Because of the recent development of network technologies such as ATM (Asynchronous Transfer Mode), broader channel bandwidth is becoming available everywhere in the world wide networks. As one of the new technologies to make good use of such broadband channel, dynamic relocation of databases through networks, which we call database migration, will soon become a powerful and basic database operation of practical use. We discuss our proposal of a distributed database system, DB-MAN (distributed database system based on DataBase Migration in ATM Networks), which takes advantage of database migration in virtual LANs (local area networks) of ATM networks. DB-MAN has two notable mechanisms: a mechanism for selecting the transaction processing method and a mechanism for concurrency control with database migration. The former, is a mechanism which chooses the more efficient method between two transaction processing methods: the conventional method based on the two phase commit protocol and our method employing database migration. The latter is a mechanism to prevent the transaction processing throughput from deteriorating in environments where data contention is a significant factor. Then we show simulation results regarding performance comparison between our proposed system and the conventional distributed database system based on the two phase commit protocol. The obtained results demonstrate that effective use of database migration gives higher performance than that of the conventional system.

Shojiro Nishio

Papers

Secured energy-aware sleep scheduling algorithm in duty-cycled sensor networks

A user customized selection and categorization for broadcast data

A bilingual dictionary extracted from the Wikipedia link structure

A time-out based resilient token transfer algorithm for mutual exclusion in computer networks

DB-MAN: a distributed database system based on database migration in ATM networks