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

Despite the drop in prices, storage cost is still a major cost factor in large scale database applications, such as data warehouses. Data compression is needed to reduce the cost. Many data compression techniques have been proposed and the issue of database compression has been discussed. Conventional data compression techniques require that compressed data be decompressed before read operations or write operations can be carried out. As a result, it is not practical to compress databases in active use using the conventional data compression techniques. In this chapter, we propose a database compression technique which needs only partial decompression for read operations and no decompression for write operations. It is suitable for databases in active use and can be used to compress data in relational databases. The proposed technique finds rules in a relational database using the Apriori Algorithm and store data using rules to achieve high compression ratios. The rules are in turn stored in a deductive database to enable easy data access.

Database compression with data mining methods

In the location dependent information services, not only estimation of the coordinates of a user (coordinate estimation), but also existence check of a user whether he/she exists in a certain area or not (area estimation) are required. However, no existing research on location estimation method using wireless communication treats both coordinate estimation and area estimation simultaneously with high accuracy. Moreover, the estimation accuracy is low if a user exists at a area where less nodes of wireless communication has its coordinates. In this paper, we propose a novel self-localization method using propagation of geographical existence probability distribution based on RSSI. Proposed method can deal with coordinate and area estimation simultaneously. It also improves the accuracy of estimation by using propagation of existence probability distribution instead of propagating the coordinate estimation result. The simulation result shows that the proposed method can raise recall to 74% keeping precision as 97% for an area estimation.

A Self-Localization Method Using Propagation of Existence Probability

Recently, a lot of researchers have directed their attention to mobile sensor networks that are constructedby sensor nodes with a moving facility. Mobile sensor networks enable to construct a wide-range sensingsystem by the cooperative behaviours of a small number of mobile sensors. However, because radiocommunication range of the nodes does not cover the whole sensing area, every node has to move closer tothe sink to deliver its sensor readings. Thus, the power consumption to deliver the sensed data to the sinkbecomes large. We previously proposed two mobile sensor control methods to reduce the powerconsumption by employing push-based broadcast, named the MST (Moving-distance-based StaticTopology) and the SR-N (Shortest Route with Negotiation) methods for sparse mobile sensor networks. Inthis paper, we propose the MST/NFD (MST with Node Failure Detection) and the SR-N/NFD (SR-N withNode Failure Detection) methods as extensions of the MST and the SR-N methods to detect node failures.We also conducted simulation experiments to evaluate the performance of our methods and confirmed thatthe MST/NFD method is more robust over node failures than the SR-N/NFD method, and that the SRN/NFD method can achieve the high throughput than the MST/NFD method.

Node control methods to reduce power consumption using push-based broadcast for mobile sensor networks

In this paper, we propose a data forwarding method for efficient data gathering in wireless sensor networks with multiple mobile sinks which freely move in the target region. In our proposed method, each sensor node forwards data packets based on information on connectivity (the status of connection with mobile sinks). As the way to share information on connectivity among neighboring nodes, we integrate push-based and pull-based mechanisms we have proposed in [5]. The integrated mechanism utilizes advertisement from the nodes with good connectivity together with request from the nodes which hold data to transmit. By the complementary use of these mechanisms, our proposed method reduces the traffic for sharing information while realizing an efficient data forwarding.

Integration of Push-Based and Pull-Based Connectivity Status Sharing for Efficient Data Forwarding towards Mobile Sinks in Wireless Sensor Networks

Recent advances in network and computer technologies have led to the development of the mobile computing environment. In this environment, the location management is one of the most important issues. In this paper, for reducing the traffic volume of the location management, we propose a hierarchical location management method. This method divides a network into multiple groups (areas) where each area has at least one router. The locations of mobile hosts in an area are managed within the area, independently from those in other areas. Furthermore, the most suitable location management method for an area is used in this area. We also show results of simulation experiments for verifying the efficiency of the method. These results show that our proposed method can reduce the traffic volume of the location management.

https://ieeexplore.ieee.org/iel3/4892/13485/00619946.pdf

Shojiro Nishio

Papers

Database compression with data mining methods

A Self-Localization Method Using Propagation of Existence Probability

Node control methods to reduce power consumption using push-based broadcast for mobile sensor networks

Integration of Push-Based and Pull-Based Connectivity Status Sharing for Efficient Data Forwarding towards Mobile Sinks in Wireless Sensor Networks

A location management method using network hierarchies