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

Wikipedia, a collaborative Wiki-based encyclopedia, has be- come a huge phenomenon among Internet users. It covers huge number of concepts of various fields such as Arts, Geography, History, Science, Sports and Games. Since it is becoming a database storing all human knowledge, Wikipedia mining is a promising approach that bridges the Semantic Web and the Social Web (a. k. a. Web 2.0). In fact, in the previ- ous researches on Wikipedia mining, it is strongly proved that Wikipedia has a remarkable capability as a corpus for knowledge extraction, espe- cially for relatedness measurement among concepts. However, semantic relatedness is just a numerical strength of a relation but does not have an explicit relation type. To extract inferable semantic relations with ex- plicit relation types, we need to analyze not only the link structure but also texts in Wikipedia. In this paper, we propose a consistent approach of semantic relation extraction from Wikipedia. The method consists of three sub-processes highly optimized for Wikipedia mining; 1) fast pre- processing, 2) POS (Part Of Speech) tag tree analysis, and 3) mainstay extraction. Furthermore, our detailed evaluation proved that link struc- ture mining improves both the accuracy and the scalability of semantic relations extraction.

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Wikipedia Link Structure and Text Mining for Semantic Relation Extraction Towards a Huge Scale Global Web Ontology

Data replication is effective for improving data availability in ad hoc networks. In an environment where data updates occur, replicas of a data item may be inconsistent. To solve this problem, quorum based consistency management is a promising approach. To reduce communication overhead, it is better to make the number of mobile hosts in each quorum small. In this paper, we propose a consistency management method that constructs quorums with fewer mobile hosts.

Consistency Management among Replicas Using a Quorum System in Ad Hoc Networks

Top-k query processing for replicated data in mobile peer to peer networks

In mobile ad hoc networks (MANETs), it is effective to retrieve data items using top-k query. However, accurate results may not be acquired in environments when malicious nodes are present. In this paper, we assume that malicious nodes attempt to replace necessary data items with unnecessary ones (we call these data replacement attacks), and propose methods for top-k query processing and malicious node identification based on node grouping in MANETs. In order to maintain the accuracy of the query result, nodes reply with k data items with the highest score along multiple routes, and the query-issuing node tries to detect attacks from the information attached to the reply messages. After detecting attacks, the query-issuing node tries to identify the malicious nodes through message exchanges with other nodes. When multiple malicious nodes are present, the query-issuing node may not be able to identify all malicious nodes at a single query. It is effective for a node to share information about the identified malicious nodes with other nodes. In our method, each node divides all nodes into groups by using the similarity of the information about the identified malicious nodes. Then, it identifies malicious nodes based on the information on the groups. We conduct simulation experiments by using a network simulator, QualNet5.2, to verify that our method achieves high accuracy of the query result and identifies malicious nodes.

Top-k Query Processing and Malicious Node Identification Based on Node Grouping in MANETs

Because of its ability to provide the collision-free packet transmission regardless of the traffic load, TDMA has been applied effectively in ad hoc networks. Until now, assuming a stationary ad hoc network, a TDMA slot assignment protocol which achieves the high channel utilization has been proposed. In this paper, we extend the protocol to accomodate the dynamic topology change due to the movement of nodes. In our protocol, since each node autonomously detects a change in network topology and assigns a slot to itself, the high channel utilization in the previous protocol can be kept even when nodes in a network move. Furthermore, we verify the effectiveness of the protocol by simulation experiments. The results show that our protocol improves channel utilization in comparison to conventional protocols.

Shojiro Nishio

Papers

Wikipedia Link Structure and Text Mining for Semantic Relation Extraction Towards a Huge Scale Global Web Ontology

Consistency Management among Replicas Using a Quorum System in Ad Hoc Networks

Top-k query processing for replicated data in mobile peer to peer networks

Top-k Query Processing and Malicious Node Identification Based on Node Grouping in MANETs

An efficient TDMA slot assignment protocol in mobile ad hoc networks