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

In this paper, we propose a new framework for ubiquitous computing by rule-based, event-driven I/O (input/output) control devices Our approach is flexible and autonomous because it employs a behavior-description language based on ECA (Event, Condition, Action) rules with simple I/O control functions We have implemented a prototype ubiquitous device with connectors and several sensors to show the effectiveness of our approach

Ubiquitous Chip: A Rule-Based I/O Control Device for Ubiquitous Computing

In mobile ad hoc networks, a mobile host holding data items frequently accessed by other hosts needs to transmit them many times and consumes more power than other hosts. In this paper, we propose replica allocation methods for not only improving data availability but also balancing the power consumption among mobile hosts. In these methods, each mobile host replicates data items taking into account their access frequencies, the numbers of their replicas, and the host's remaining battery power. We present simulation results to evaluate the performance of our proposed methods.

Data Replication Considering Power Consumption in Ad Hoc Networks

In wearable computing environments, a wearable computer runs various applications using various sensors (wearable sensors). In the area of context awareness, though various systems using accelerometers have been proposed to recognize very minute motions and states, energy consumption was not taken into consideration. We propose a context-aware system that reduces energy consumption. In life, the granularity of required contexts differs according to the situation. Therefore, the proposed system changes the granularity of cognitive contexts of a user's situation and supplies power on the basis of the optimal sensor combination. Higher accuracy is achieved with fewer sensors. In addition, in proportion to the remainder of power resources, the proposed system reduces the number of sensors within the tolerance of accuracy. Moreover, the accuracy is improved by considering context transition. Even if the number of sensors changes, no extra classifiers or training data are required because the data for shutting off sensors is complemented by our proposed algorithm. By using our system, power consumption can be reduced without large losses in accuracy.

/pdf/a-context-aware-system-that-changes-sensor-combinations-42zfp5xel5.pdf

A Context-Aware System that Changes Sensor Combinations Considering Energy Consumption

This paper proposes a multimedia presentation system Harmony which can deal with temporal and active media such as motion video, computer animation, and music sounds. Harmony is based on the notion of a hyperobject which integrates a hypertext system with an object-oriented framework. Each object is considered a node as is usual in hypertext systems, and the relations between objects are represented as links between nodes. Harmony extends the ordinary notion of a link. A link in Harmony consists of three components: objects connected by the link, conditions specifying when the link is navigated, andmessages sent to the target object. In addition to such an extension, each object can include internal objects called subobjects which specify parts of an object and can become a source/destination of a link. Furthermore, the notion of a group object is introduced to represent a synchronization of parallel displayed media information. Through these extended notions of a hypertext model, temporal and active media can be easily handled through the description of temporal relations from the media in hypermedia documents. Based on the design of Harmony, we have implemented a prototype multimedia presentation system which deals with text, music, graphics, motion video, and computer animation as objects. Harmony consists of three subsystems: link manager subsystem (Harmony/LM), user interface subsystem (Harmony/UI), and object-oriented database (Harmony/DB), where a commercial objectoriented database was employed as the Harmony/DB. Scenario viewer was developed for displaying the structure of scenario in a tree graph which can reduce a cognitive overhead in a hypermedia document. The whole system was constructed based on C++ language.

Multimedia Presentation System Harmony with Temporal and Active Media.

This paper first reveals the relationship between Inverse Document Frequency (IDF), a global term weighting scheme, and information distance, a universal metric defined by Kolmogorov complexity. We concretely give a theoretical explanation that the IDF of a term is equal to the distance between the term and the empty string in the space of information distance in which the Kolmogorov complexity is approximated using Web documents and the Shannon-Fano coding. Based on our findings, we propose N-gram IDF, a theoretical extension of IDF for handling words and phrases of any length. By comparing weights among N-grams of any N, N-gram IDF enables us to determine dominant N-grams among overlapping ones and extract key terms of any length from texts without using any NLP techniques. To efficiently compute the weight for all possible N-grams, we adopt two string processing techniques, i.e., maximal substring extraction using enhanced suffix array and document listing using wavelet tree. We conducted experiments on key term extraction and Web search query segmentation, and found that N-gram IDF was competitive with state-of-the-art methods that were designed for each application using additional resources and efforts. The results exemplified the potential of N-gram IDF.

/pdf/n-gram-idf-a-global-term-weighting-scheme-based-on-3j5kbomqd8.pdf

Shojiro Nishio

Papers

Ubiquitous Chip: A Rule-Based I/O Control Device for Ubiquitous Computing

Data Replication Considering Power Consumption in Ad Hoc Networks

A Context-Aware System that Changes Sensor Combinations Considering Energy Consumption

Multimedia Presentation System Harmony with Temporal and Active Media.

N-gram IDF: A Global Term Weighting Scheme Based on Information Distance