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 years, the miniaturization of the computers is advanced as the technology develops. However, there is a limit of the miniaturization of the keyboard because of the usability of easiness of the key touch. Therefore, in this paper, we proposes the method to miniaturize by excluding the left half of the keyboard. The proposal system calculates the input word at each pause of the word, and displays the candidates according to priority. It becomes possible to reduce the size of the keyboard to half without decreasing the usability by using our system.

A Input Complementary Method for One-Handed Keyboard

We recently have seen illumination using Light Emitting Diodes (LEDs) on the street. The current illumination is difficult to control of flexibly and dynamically. In response to this problem, we propose a method controlling numerous LEDs collectively, dynamically, and flexibly by using a projector and small devices with optical sensors. We designed and implemented a small device to achieve our mechanism, which has an LED, an optical sensor, and a microcomputer to react to the light illuminated from a projector. Moreover, it has functions to self adjust the threshold and recognize optical commands, which are required for collective control. We discuss how effective our proposed device was in a study to evaluate it and in field tests done at actual events.

Design and implementation of a ubiquitous optical device controlled with a projector

In mobile comput ing environments , the location of each mobile host should be managed by the networking sys tem to realize communica t ion among mobile hosts. Thus , traffic is caused not only by the communica t ion but also by the location management . In this paper , we propose a loca t ion management method for improving the mobile communica tion th roughpu t based on grouping of touters. Fi rs t , this method divides the routers in a fixed network into mul t ip le groups. Then , the routers in each group manage the location informat ion independent ly of other groups by choosing a locat ion managemen t s t ra tegy from several convent iona l strategies. We show the results of our s imulat ion experiments to compare the traffic vo lume of our m e t h o d and Mobile-IP. T h e results show tha t our method can great ly reduce the traffic volume compared with Mobile-IP. We also discuss the communica t ion overhead of our m e t h o d and show tha t our method can be pract ical ly used in the real environment . Moreover, we address some problems of our method and show possible extensions to solve them. K e y w o r d s : mobile comput ing environment , locat ion management, ne twork protocol, Mobile-IP, routing pro tocol

Location management of mobile hosts by grouping routers

A search engine evaluation model that considers over seventy performance and feature parameters is presented. The design of a web-based system that allows the user to tailor the model to his/her own preference, and to evaluate search engines of interest, is introduced. The results presented to the user identify the most suitable search engine that suits his/her needs.

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Finding the search engine that works for you

The mobile computing paradigm introduces new technical issues in the area of database systems. In a mobile environment, the location of mobile users is a kind of system data that is referred most frequently. In mobile environments, location data are stored in databases. However, as the location of user is a frequently changing piece of data, queries on location data are prone to inconsistency. Aggregate queries are among the type of queries that will suffer greatly. In this paper, we propose the two-step locking protocol for location databases that can guarantee aggregate queries to get consistent results. We show that our proposed protocol performs much better than the conventional single-step locking protocol if used for location databases in mobile environment.

Shojiro Nishio

Papers

A Input Complementary Method for One-Handed Keyboard

Design and implementation of a ubiquitous optical device controlled with a projector

Location management of mobile hosts by grouping routers

Finding the search engine that works for you

Performance evaluation of two step locking protocol for location databases in mobile environment