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

A Collaborative Web Browsing System for Multiple Mobile Users by Using Web Page Splitting

Car navigation systems provide the best route to a destination quickly and effectively. However, during daily driving, this information is not necessary since drivers already know the route to the destination very well. In addition, it is time-consuming for drivers to input the destination. Thus, our research group has proposed a new car navigation system that provides information related to the destination by predicting the user's destination automatically. We propose the use of a new method that predicts the destination on the basis of the driving trajectory and the contexts in which the user drives. A system that uses our method knows the destination without user interaction and provides information related to the correct destination.

A destination prediction method using driving contexts and trajectory for car navigation systems

To improve data accessibility in ad hoc networks, we have proposed a few methods of replicating data items on mobile hosts by considering the data access frequencies from mobile hosts to each data item and the network topology. In this paper, we consider the stability of radio links among mobile hosts to enhance our previously proposed methods. We show that the newly proposed methods are more efficient by simulation results.

Data replication methods based on the stability of radio links in ad hoc networks

Due to its ability to provide collision-free packet transmission regardless of the traffic load, TDMA (Time Division Multiple Access) has been applied in ad hoc sensor networks. We have previously proposed a TDMA slot assignment protocol that utilizes the channel bandwidth effectively in ad hoc sensor networks. However, in this protocol, the channel bandwidth cannot be fully utilized because the frame length across the whole network tends to increase. In this paper, we extend this protocol to further improve channel utilization. The extended protocol prevents the excessive increase of unassigned slots by minimizing each node's frame length. Furthermore, we verify the effectiveness of our proposed protocols by simulation experiments. The results show that our extended protocol improves channel utilization dramatically in comparison with our previous protocol.

An adaptive TDMA slot assignment protocol in ad hoc sensor networks

Aiming at finding the $k$ nodes with the highest readings among sensor nodes, top- $k$ query is a very important and useful query in wireless sensor networks (WSNs). Current studies about top- $k$ query in WSNs mainly focus on always-on WSNs (A-WSNs) where sensors always stay awake and little research pays attention to top- $k$ query in duty-cycled WSNs (D-WSNs) in which sensors are dynamically awake and asleep to conserve energy. In this paper, we originally analyze the research issues when implementing top- $k$ query in D-WSNs and then propose the DRC-WSNs [D-WSNs with data replication (DR) and connected $k_{c}$ -neighborhood (CKN)] scheme for performing top- $k$ query. Extensive theoretical analysis and simulation results provide the following two new insights regarding top- $k$ query in D-WSNs: 1) Implementing top- $k$ query in DRC-WSNs can achieve the best tradeoff with respect to query data accessibility and query cost (total energy consumption and query response time), compared with implementing top- $k$ query in DC-WSNs (D-WSNs with only CKN) and A-WSNs; and 2) the query data accessibility and the query cost of DRC-WSNs, DC-WSNs, and A-WSNs can be tuned by varying the number of neighbor nodes that the data are replicated to during DR, the $k_{c}$ in CKN, and the total sensor nodes number.

Shojiro Nishio

Papers

A Collaborative Web Browsing System for Multiple Mobile Users by Using Web Page Splitting

A destination prediction method using driving contexts and trajectory for car navigation systems

Data replication methods based on the stability of radio links in ad hoc networks

An adaptive TDMA slot assignment protocol in ad hoc sensor networks

Insights of Top- $k$ Query in Duty-Cycled Wireless Sensor Networks