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

With the increase of data generation in distributed fashions such as peer-to-peer systems and sensor networks, top-k query processing which returns only a small set of data that satisfies many users' preferences, becomes a substantial issue. When data are periodically updated in each epoch e.g., weather information, without any techniques, a naive solution is to aggregate all data and their updates to ensure the correctness of final answers, however, it is too costly in terms of data transfer especially for data aggregator nodes. In this paper, we propose a top-k monitoring query processing method in 2-tier distributed systems based on a publish-subscribe scheme. A set of top-k subscriptions specifying summary scope of users' interests is informed to aggregators to limit the number of transferred data records for each epoch. In addition, instead of issuing subscriptions of all queries, our method identifies a small set of minimal subscriptions resulting in lower communication overhead. Our experiments show that our technique is efficient and outperforms other comparative reactive methods.

Communication-efficient preference top-k monitoring queries via subscriptions

In mobile sensor networks (MSNs), since sensor nodes and wireless networks are highly resource constrained, it is required to manage sensor data in flexible and efficient manners. Under the MEXT research project entitled "Studies on Efficient Data Processing Techniques for Mobile Sensor Networks," we have conducted researches on data management issues in MSNs. In this paper, we report some of our achievements in a sub-area of this project, which addresses data allocation for efficient query processing in MSNs. In particular, we first show our achievements on how to effectively allocate sensor data on mobile nodes and how to efficiently process top-k and k-nearest neighbor queries on the allocated data. Then, we also show our achievements on how to quantify the impacts of mobility on data availability and data dissemination in MSNs.

Data Allocation Techniques for Mobile Sensor Networks

In mobile ad hoc networks, when a mobile host receives a data request from another host, the host usually transmits the requested data item by unicast. However, if mobile hosts hold data items that are frequently requested by others, they have to transmit the data items many times and consume a large amount of power. In this paper, we propose a data transmission method for not only maintaining data availability but also reducing traffic for data access. In our proposed method, each mobile host sends a data request attached with the deadline to receive the requested data item by the determined time. Moreover, each mobile host collects multiple requests for data items and transmits the requested data items by multicast, and thus, reduces data traffic. We also show the results of simulation experiments regarding the performance of our proposed method.

A Data Transmission Method Using Multicast in Mobile Ad Hoc Networks

In recent years, there has been an increasing interest in broadcast database systems, where the server periodically broadcasts contents of a database to mobile clients such as PDAs. There are three query processing methods in the broadcast database system: (i) the server processes a query and then broadcasts the query result to the client; (ii) the client stores all data that are necessary in processing the query and then processes it locally; and (iii) the server and the client collaborate in processing the query. Since the performance of each method changes according to the system situation, such as query frequency, it is difficult to decide the optimal method among them statically. In this paper, we propose a new query processing method which dynamically changes the query processing method based on query frequency. This method improves not only the response time but also the success rate of query processing compared with the traditional methods.

Shojiro Nishio

Papers

Communication-efficient preference top-k monitoring queries via subscriptions

Data Allocation Techniques for Mobile Sensor Networks

A Data Transmission Method Using Multicast in Mobile Ad Hoc Networks

A Query Processing Method Considering Query Frequency for Broadcast Database Systems

Replication Strategy Depending on Access Frequency in Peer-to-Peer Networks