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

/pdf/data-mining-concepts-and-techniques-4dtvdfkvmi.pdf

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

Purpose – It has become common for children to browse web pages. However, there is no web browser that takes into account children's characteristics on information acquisition. Therefore, even though such general pages have a variety and detailed information, children cannot effectively use the internet with current web browsers, e.g. they have difficulty in understanding the contents and easily get bored when browsing general pages. The purpose of this paper is to propose a children‐oriented web browser, which aims to keep children's interest on pages and help them understand the contents of the pages.Design/methodology/approach – The paper designed and implemented a web browser for children using a bubble metaphor, which converts general pages into a children‐friendly presentation. The browser is displayed in an undersea scene and presents contents of a web page in bubbles of different sizes, speeds, and colors. Furthermore, it presents the details of the content in a picture book style in a way that ch...

Web browser for children using bubble metaphor

A scheduling of database migration in wide‐area networks

Top- queries, which retrieve the most preferable data objects, have been receiving
much attention. An emerging challenge is to support efficient top- query
processing in a wireless distributed network. In this study, we investigated how
to process multidimensional top- queries efficiently in a wireless multihop network. 
A major challenge for multidimensional top- queries is that answers for
different users are typically different, because each user has a unique preference
and search range. Meanwhile, it is desirable for wireless networks to reduce unnecessary
traffic even if users issue top- queries with their own unique preferences. 
Therefore, we address the above problem and propose a top- query processing
method in wireless multihop networks, called ClusTo. ClusTo performs a
novel clustering scheme for multidimensional top- query processing and routes
queries based on the cluster while guaranteeing the user’s specified search range. 
Moreover, ClusTo takes a dynamic threshold approach to suppress unnecessary
query transmissions to nodes which do not contribute to top- data retrieval. Extensive
experiments on both real and synthetic data have demonstrated that ClusTo
outperforms existing methods in terms of traffic and delay.

https://downloads.hindawi.com/journals/misy/2015/657431.pdf

Efficient Multidimensional Top- Query Processing in Wireless Multihop Networks

This paper presents an investigation of children's and adults' preferences on Web pages in order to reveal special preferences of children. For this aim, we conducted an experiment that asked 34 children and 21 adults to score Web pages (whether the pages are preferable for them or not), and we analyzed the scores of the pages from three aspects, calculating statistics of the pages' scores, classifying the pages based on their scores, and applying a children-oriented re-ranking method. These results show that children-friendly Web pages should be not only in visually appealing presentation but also easily understandable to grasp important contents in the pages for children by showing images and titles related to the contents.

Shojiro Nishio

Papers

Web browser for children using bubble metaphor

A scheduling of database migration in wide‐area networks

Efficient Multidimensional Top- Query Processing in Wireless Multihop Networks

How Different are Preferences of Children and Adults on Web Pages

Discovering Charactertistic Rules from Deductive Databases.