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

Recent advances in computer and wireless communication technologies have led to an increasing interest in ad hoc networks which are constructed of only mobile hosts. In this paper, we propose two cache invalidation methods in ad hoc networks where each data item is updated at inconstant intervals. In the first method, when a mobile host holding an original data item updates the data item, it broadcasts an invalidation report to all connected mobile hosts. In the other method, when two mobile hosts are connected, they rebroadcast invalidation reports received before to newly connected mobile hosts. Our proposed methods reduce the number of accesses to invalid cached data items which have been updated and the number of roll backs caused by such invalid accesses. We also show the results of simulation experiments regarding the performance evaluation of our proposed methods.

Cache Invalidation for Updated Data in Ad Hoc Networks

This paper proposes a knowledge representation DOT (Deductive and Objectoriented Term representation), which employs extended terms to describe the properties of objects using dot notation and IS-A relation. Its semantics is provided on a quasi-ordering structure called DOT algebra. DOT presents a simple framework for inheritance and becomes an inference engine for knowledge-bases constructed from the IS-A relation. We discuss how to resolve three fundamental problems of DOT algebra, and demonstrate that a class of queries called bounded query can be evaluated by those resolution methods, in which a regular expression of automata theory assumes a central role.

DOT: A term representation using DOT algebra for knowledge-bases

In ad hoc networks, to acquire only necessary data items, it is effective that each mobile node retrieves data items using a top-k query, in which data items are ordered by the score of a particular attribute, and the query-issuing mobile node acquires data items with the k highest scores. In this paper, we propose a message processing method for a top-k query for reducing traffic and keeping the accuracy of the query result, i.e., guaranteeing that data items with the k highest scores in the entire network are acquired. When a mobile node transmits query and reply messages, our method can be used to reduce candidates of data items included in the top-k result to prevent unnecessary data transmissions. Moreover, if a mobile node detects a disconnection of a radio link, it searches for an alternative path to transmit the reply message to the query-issuing node. We present simulation results to evaluate the performance of our proposed method.

A Message Processing Method for Top-k Query for Traffic Reduction in Ad Hoc Networks

Because of advances in information technologies, car navigation systems have come into widespread use as useful tools to guide drivers where they want to go. Conventional car navigation systems present the most suitable route according to a destination input into the system. However, since the required operation to input the destination costs so much, users do not usually use car navigation systems for daily driving. In this paper, to exploit the effective functions of car navigation systems, we propose a new system that automatically predicts user purpose and destination. The proposed car navigation system presents various information based on predicted purpose without interaction from users.

Design of a Car Navigation System that Predicts User Destination

Recently, various schemes for broadcasting continuous media data such as audio and video have been studied. Some of them have focused on reducing the waiting time of clients under the condition that clients can play data without interruption from beginning to end. These schemes usually employ multiple channels to broadcast continuous media data. However, clients for most broadcast systems such as wireless LAN, DVB and ISDB-T cannot receive data from multiple channels concurrently. In this paper, we propose and evaluate a scheduling scheme to reduce the waiting time of clients with a single channel. Preliminary results suggest that this scheme outperforms existing schemes.

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Shojiro Nishio

Papers

Cache Invalidation for Updated Data in Ad Hoc Networks

DOT: A term representation using DOT algebra for knowledge-bases

A Message Processing Method for Top-k Query for Traffic Reduction in Ad Hoc Networks

Design of a Car Navigation System that Predicts User Destination

A scheduling scheme for continuous media data broadcasting with a single channel