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 this paper, we propose two updated data dissemination methods to not only reduce the number of accesses to old replicas, but also to improve the data accessibility in ad hoc networks where data items are updated regularly. In the first method, when a mobile host updates a data item, it disseminates the updated data item after the flooding of with invalidation reports. In the second method, two newly connected mobile hosts disseminate updated data items with each other after the flooding with invalidation reports.

Updated data dissemination methods for updating old replicas in ad hoc networks

In this paper, we propose a novel energy-efficient data transmission reduction method for periodical data gathering in Wireless Sensor Networks (WSNs). Our method exploits overhearing, which is a typical characteristic of wireless communications. In our method, each node in a WSN autonomously determines whether its own reading is redundant or not by using the overheard packets transmitted by its neighbors. The redundancy of a reading is evaluated by a simple spatial interpolation using the readings of its neighbors. If the reading is determined as redundant, the node stops transmitting it. Furthermore, we present an evaluation based on simulation experiments with various parameters and show that our method suppresses data transmissions and reduces total energy consumption.

Overhearing-Based Data Transmission Reduction for Periodical Data Gathering in Wireless Sensor Networks

As of now, it is not uncommon for one to use multiple computers in separate places such as at home, office or school. A number of applications currently exist to allow a user to easily access and control these computers remotely via a notebook computer or web page. Unfortunately, even with such solutions, it is rather inconvenient, for example, to try accessing your computer while walking downtown or riding a train. On the other hand, considering that cellular phones have been accepted as multi-function devices capable of karaoke, digital photography and web browsing in countries like Japan, it may prove beneficial to support remote PC access on a cellular phone. With this in mind, we created Rajicon, a system to provide an easy to use interface to remotely operate a PC's GUI operating system via a cellular phone. Rajicon supports image based navigation of a remote PC's desktop and mouse/key operations. In addition, Rajicon provides functions for keyboard shortcuts, specialized window adjustment operations and text extraction. We have performed extensive evaluations with five subjects and discuss the results in this paper. We also discuss the lead author's latest adaption of Rajicon to a PDA.

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Rajicon: remote PC GUI operations via constricted mobile interfaces

In ad hoc networks, it is effective that each mobile host creates replicas of data items held by other hosts for improving data accessibility. However, a mobile host holding data items frequently accessed by other hosts needs to transmit them many times and consumes more power than other hosts. In this paper, we propose a replica allocation method for not only improving data accessibility but also balancing the power consumption among mobile hosts. In this method, each mobile host replicates data items considering their access frequencies, the numbers of their replicas, and the remaining amount of its battery. We present simulation results to evaluate the performance of our proposed method.

Replica allocation considering power consumption in mobile ad hoc networks

In recent years, there has been an increasing interest in P2P (Peer-to-Peer) streaming environments. In P2P streaming environments, compared with the traditional server-client model, the load of the server can be reduced by transmitting the divided streaming data (pieces) among clients. However, when bandwidths between clients are narrow, interruption occurs since clients cannot receive the necessary piece of data until the time to play it. In this paper, we propose a data reception method to reduce interruption time in P2P streaming environments. Our proposed method calculates the importance of each piece which takes into account the immediacy and the scarcity of the piece. The evaluation results show that our proposed method can reduce the interruption time for peers in a P2P environment compared with conventional methods.

Shojiro Nishio

Papers

Updated data dissemination methods for updating old replicas in ad hoc networks

Overhearing-Based Data Transmission Reduction for Periodical Data Gathering in Wireless Sensor Networks

Rajicon: remote PC GUI operations via constricted mobile interfaces

Replica allocation considering power consumption in mobile ad hoc networks

A Data Reception Method to Reduce Interruption Time in P2P Streaming Environments