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 an Intelligent Transport System (ITS), information sharing based on inter-vehicle communication is effective for improving data availability. In this paper, we propose a data dissemination method for sharing data items among vehicles, which increases the opportunity for vehicles to acquire more fresh data items that users request. In our proposed method, vehicles are grouped according to their locations, and movement directions that are estimated from their route information. Then, data items that are more likely to be accessed in the near future are disseminated between two groups. We also present simulation results to evaluate the performance of our proposed method.

Effective data dissemination for information sharing based on inter-vehicle communication

Due to the recent development of computer and network technologies, there has been increasing interest in peer-to-peer (P2P) networks. To reduce query messages for information retrieval in P2P networks, many conventional studies proposed methods that categorize similar data items and cluster peers that have data items in the same category. Some of them aim to construct clusters dynamically when the number of queries for similar data items exceeds the predetermined threshold. However, these methods have a critical problem that the threshold is fixed for any queries. The fixed threshold cannot perform cluster construction at appropriate timings since they depend on characteristics of query generation. In this paper, we propose a dynamic cluster construction method based on characteristics of query generation. Our method can reduce query messages by constructing dynamic clusters at more appropriate timings. The results of simulation experiments show that our method can reduce query messages compared with an existing method.

A Dynamic Cluster Construction Method Based on Characteristics of Query Generation in Peer-to-Peer Networks

To make a weather observation or see the picture of a disaster, sensor networks, in which sensors such as temperature or humidity communicate, have recently attracted attention. Several schemes are proposed to collect data generated by sensors effectively. However, when there are many sensors, it takes long time to collect sensor data since information traffic is large. In this paper, we proopse a data collecting scheme that conjectures sensor data and broadcasts them. In our proposed scheme, sensors that have data vastly differ from conjectured data only transmit their sensor data. Accordingly, information traffic is reduced and the necessary time for collecting data becomes short. Our evaluations show that our proposed scheme can collect data faster than a conventional collecting scheme when there are many sensors.

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A Data Collecting Scheme using Broadcast Delivery in Sensor Networks

On Locking Protocols in Object-Oriented Database Systems

In order to improve Semantic Web Mining, as a precondition, there have to be enough data that are “well”-structured by linking to other web resources. However, Semantic Web data in real world, such as RSS and Dublin Core, are just semi-structured documents in most cases, because the main part of the content is still mixed with text data. In this paper, we propose a new Web Mining method based on Personal Ontology, a concept dictionary in the local machine personalized for each user which maps to web resource. Our approach accomplished Semantic Web Mining for semi-structured data such as RSS.

Shojiro Nishio

Papers

Effective data dissemination for information sharing based on inter-vehicle communication

A Dynamic Cluster Construction Method Based on Characteristics of Query Generation in Peer-to-Peer Networks

A Data Collecting Scheme using Broadcast Delivery in Sensor Networks

On Locking Protocols in Object-Oriented Database Systems

A web mining method based on personal ontology for semi-structured RDF