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 mobile ad hoc networks (MANETs), it is effective for mobile nodes to retrieve data items using top-k queries, in which data items are ordered according to a particular attribute score, and the query-issuing node acquires the data items with the k highest scores. However, accurate results may not be acquired in environments where malicious nodes are present. In top-k queries, it is important to neutralize attacks in which malicious nodes attempt to replace necessary data items with unnecessary ones (we call these, data replacement attacks). In this paper, we propose methods for top-k query processing and malicious node identification against data replacement attack in MANETs. In the top-k query processing method, in order to maintain accuracy of the query result, nodes reply with data items with the k highest scores, along multiple routes. Moreover, to enable detection of data replacement attacks, reply messages include information on the route along which reply messages are forwarded, and thus the query-issuing node can know the data items that properly belong to the message. In the malicious node identification method, the query-issuing node first narrows down the malicious node candidates, using the received message information, and then requests information on the data items sent by these candidates. In this way, the query-issuing node can identify the malicious node. Finally, we verify, through simulation experiments, that the proposed top-k query processing method achieves high accuracy of the query result, and that the malicious node identification method effectively identifies a malicious node.

Top-k Query Processing and Malicious Node Identification against Data Replacement Attack in MANETs

Recently, many studies exploit mobile sinks to construct large-scale databases for environmental observations or weather forecasting. Mobile sinks travel in the sensing area that sensors are deployed, collect data from each sensor, and uploads them to sensor databases. By using mobile sinks, we can reduce communication traffic and maintenance cost compared with those for wireless sensor networks (WSNs). Some methods to give fairness to data collection for sensors have been proposed. However these methods give hard restrictions for sensor data collection. Therefore, the mobile sink does not always collect data from sensors even when it can collect. This causes a fewer amount of data collection. In this paper, we propose a communication protocol to increase the data amount that the mobile sink collects considering fairness of communication time. Our proposed protocol ensures fairness by giving an upper limit to the first communication time for a sensor. After that, the mobile sink collects data from the sensor again if the mobile sink cannot find any other sensors. Our simulation results demonstrated that our proposed protocol can ensure fairness of the communication time and can increase the amount of collected data.

A Communication Protocol to Improve Fairness and Data Amount on Sensor Data Collection with a Mobile Sink

In wearable computing environments, an optical see-through head mounted display (HMD) is one of the most suitable wearable devices for displaying information. Although the HMD can display information without interfering with the user's view, the displayed information is difficult to see when the HMD background is too complex or too bright. We have created an information layout method for the optical see-through HMD that takes the background into account. It determines the ideal area for displaying objects by evaluating the HMD background captured by a camera.

An Information Layout Method for an Optical See-through HMD Considering the Background

In this paper, we propose an effective mobile sensor control method, named DATFM (Data Acquisition and Transmission with Fixed and Mobile node) for sparse sensor networks. DATFM uses two types of sensor nodes, fixed node and mobile node. The data acquired by nodes are accumulated on a fixed node before being transferred to the sink node. In addition, DATFM transfers the accumulated data efficiently by constructing a communication route of multiple mobile nodes between fixed nodes. We also conduct simulation experiments to evaluate the performance of DATFM.

https://www.mdpi.com/1424-8220/9/1/327/pdf

An effective mobile sensor control method for sparse sensor networks.

The server strategy of repeatedly broadcasting data items can result in a higher throughput than sending the requested data items to individual clients. Various methods have been studied to reduce the average response time in such systems. In this paper, two strategies are presented for scheduling the broadcast program. Both strategies take into account the correlation among the requested data items. The first strategy is suitable for an environment where clients access a set of correlated data items by submitting multiple access requests at the same time. A broadcast program is composed by placing strongly correlated data items next to each other. The second strategy is suitable for an environment where clients access a set of correlated data items by submitting multiple access requests at intervals between the requests. This latter strategy places strongly correlated data items at suitable intervals in the program. Simulation studies were used to evaluate the performance of the proposed strategies.

Shojiro Nishio

Papers

Top-k Query Processing and Malicious Node Identification against Data Replacement Attack in MANETs

A Communication Protocol to Improve Fairness and Data Amount on Sensor Data Collection with a Mobile Sink

An Information Layout Method for an Optical See-through HMD Considering the Background

An effective mobile sensor control method for sparse sensor networks.

Scheduling Strategies Of Correlated Broadcast Data In Push-Based Systems