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 recent studies on concurrency control of database systems, “cautious schedulers” have been developed intensively because they avoid both abortion and rollback in the process of concurrency control. In this paper, by means of simulation studies, we will evaluate the performance of three cautious schedulers: (1) cautious two-phase locking scheduling, (2) exclusive preclaimed two-phase locking scheduling, and (3) cautious conflict serializable scheduling. The performance of these cautious schedulers is compared with that of corresponding non-cautious schedulers.

Performance Evaluation on Several Cautious Schedulers for Database Concurrency Control

To solve the secure localization problems, a number of secure localization schemes have been developed at present. However, most of these techniques cannot survive collusion attacks where a majority of malicious nodes launch colluding attacks. In this paper, we introduce a new collusion attack model called Collaborative Collusion Attack Model (CCAM) and propose a novel scheme called Two-Step Format Detection (TSFD) that is well suited to WSN which is a resource constrained environment. The TSFD has reasonable and acceptable communication cost and algorithm complexity. Through simulations, we compare the performance of TSFD with other secure localization schemes and show that TSFD has more efficient and resilient performance.

A novel secure localization scheme against collaborative collusion in wireless sensor networks

Geographical contour lines are often used for the analysis of the sensor data distribution on wide area. In this paper, we propose a method to reconstruct contour lines by collecting sensor data from widely distributed sensors. In this case, it is redundant to collect all sensors from wide area since geographically closed sensors sense similar data. We propose a sensor data collection method for peer-to-peer sensor network to realize spatial interpolation of sensor data. In our proposal, we assume hierarchical Delaunay overlay network (HDOV) to collect sensor data uniformly. In addition, using structure of HDOV, more local sensor data is collected that affect on the spatial interpolation. By this mechanism, redundant sensor data to reconstruct geographical contour lines is reduced. The simulation result shows that our proposal can reconstruct accurate geographical contour lines, reducing target node and message numbers for data collection.

An Examination of Sensor Data Collection Method for Spatial Interpolation on Hierarchical Delaunay Overlay Network

Recently, sensor databases have been used for many applications such as environment observations or weather forecasting. To construct sensor databases, some researches focus on the rounding sink approach. In the approach, rounding sinks repeatedly round in the area that sensors are deployed and collect data from each sensor. Compared with the traditional wireless sensor network approach, the rounding sink approach can reduce communication traffic since rounding sinks collect sensor data directly from sensors. Some data collection protocols to improve the effectiveness of the data collection by rounding sinks have been proposed, but they do not consider the data amount that each sensor has. Here, a problem occurs. Rounding sinks collect only a few data from a sensor even if it has many sensor data. In this paper, we propose a data collection protocol considering the data amount that each sensor has. In our proposed protocol, a rounding sink polls neighboring sensors and gets the data amount that the polled sensor has. Then, the rounding sink calculates the upper limit for the data amount to collect and collects the data up to the limit. We confirmed that our proposed protocol can give fairness to the amount of collected data.

A Communication Protocol for Sensor Database Construction by Rounding Sink

As one of the new technologies to make good use of broader channel bandwidth of recent networks, dynamic relocation of databases through networks, which we call database migration, will soon become a powerful and basic database operation of practical use. To make full use of database migration, the main memory database is considered to be one of the most significant technologies, because it allows us to perform very high speed database access which almost matches for the transmission rate of broadband networks. In this paper, we first discuss the physical database structure which realizes high speed database migration. Then, we propose the recovery methods for migratory main memory databases.

Shojiro Nishio

Papers

Performance Evaluation on Several Cautious Schedulers for Database Concurrency Control

A novel secure localization scheme against collaborative collusion in wireless sensor networks

An Examination of Sensor Data Collection Method for Spatial Interpolation on Hierarchical Delaunay Overlay Network

A Communication Protocol for Sensor Database Construction by Rounding Sink

Main memory database for supporting database migration