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

A privacy preserving association rule mining algorithm was introducedThis algorithm preserved privacy of individual values by computing scalar productMeanwhile the algorithm of computing scalar product was given and the security was analyzed

Privacy preserving association rule mining in vertically partitioned data

Ambient Intelligence (AmI) is a new paradigm in information technology aimed at empowering people's capabilities by means of digital environments that are sensitive, adaptive, and responsive to human needs, habits, gestures, and emotions. This futuristic vision of daily environment will enable innovative human-machine interactions characterized by pervasive, unobtrusive, and anticipatory communications. Such innovative interaction paradigms make AmI technology a suitable candidate for developing various real life solutions, including in the healthcare domain. This survey will discuss the emergence of AmI techniques in the healthcare domain, in order to provide the research community with the necessary background. We will examine the infrastructure and technology required for achieving the vision of AmI, such as smart environments and wearable medical devices. We will summarize the state-of-the-art artificial intelligence (AI) methodologies used for developing AmI system in the healthcare domain, including various learning techniques (for learning from user interaction), reasoning techniques (for reasoning about users' goals and intensions), and planning techniques (for planning activities and interactions). We will also discuss how AmI technology might support people affected by various physical or mental disabilities or chronic disease. Finally, we will point to some of the successful case studies in the area and we will look at the current and future challenges to draw upon the possible future research paths.

/pdf/a-survey-on-ambient-intelligence-in-healthcare-wkvwfodws9.pdf

A Survey on Ambient Intelligence in Healthcare

https://hal.archives-ouvertes.fr/hal-01272036/document

Towards an automatic early stress recognition system for office environments based on multimodal measurements

Privacy preserving becomes an important issue in the development progress of data mining techniques. Privacy preserving data mining has become increasingly popular because it allows sharing of privacy-sensitive data for analysis purposes. So people have become increasingly unwilling to share their data, frequently resulting in individuals either refusing to share their data or providing incorrect data. In turn, such problems in data collection can affect the success of data mining, which relies on sufficient amounts of accurate data in order to produce meaningful results. In recent years, the wide availability of personal data has made the problem of privacy preserving data mining an important one. A number of methods have recently been proposed for privacy preserving data mining of multidimensional data records. This paper intends to reiterate several privacy preserving data mining technologies clearly and then proceeds to analyze the merits and shortcomings of these technologies.

A Survey on Privacy Preserving Data Mining

Cloud computing seems to offer some incredible benefits for communicators: the availability of an incredible array of software applications, access to lightning-quick processing power, unlimited storage, and the ability to easily share and process information. All of this is available through your browser any time you can access the Internet. While this might all appear enticing, there remain issues of reliability, portability, privacy, and security. When our private data are out-sourced in cloud computing, we should guarantee the confidentiality and searchability of the private data. Our paper provides a new approach to avoid the disclosure of the sensitive attributes of users when user ask for service from the Service Provider (SP) in cloud computing.

A New Privacy Preserving Approach Used in Cloud Computing

Publishing the data with multiple sensitive attributes brings us greater challenge than publishing the data with single sensitive attribute in the area of privacy preserving In this paper, we propose a novel privacy preserving model based on k-anonymity called (α, β, k)-anonymity for databases (α, β, k)-anonymity can be used to protect data with multiple sensitive attributes in data publishing Then, we set a hierarchy sensitive attribute rule to achieve (α, β, k)-anonymity model and develop the corresponding algorithm to anonymize the microdata by using generalization and hierarchy We verify (α, β, k)-anonymity approach can effectively protect privacy information of individual and resist background knowledge attack in publishing the data with multiple sensitive attributes by specific example

(α, β, k)-anonymity: An effective privacy preserving model for databases

A lot of information of individuals is published by hospitals, insurance companies, medical administrations and governments, which may cause a major risk of sensitive information leakage in recent years. Thus, privacy preserving in data publishing has become an important research topics in database security field. In this paper, we propose a novel privacy preserving approach based on k-anonymity model and multidimensional model, which combines global recoding and local recoding technology and provides privacy preserving in data publishing. The novel anonymity strategy can efficiently and dynamically designate sensitive information according to the requirements of users. Then we develop an anonymous strategy algorithm which adapts to the anonymity strategy and achieves the purpose of preventing homogeneity attack and background knowledge attack. Finally, we conclude the paper and introduce the research directions for future work.

A novel privacy preserving approach for database security

Along with deep-going researches and practical applications of Wireless Sensor Network, security issues contained therein are raising a growing concern. Based on a systematic analysis of the current intrusion detection system and with integration between the mechanism of artificial immune system and multi-agent technology, the paper provides with a new model of an intrusion detection system based on artificial immune system and multi-agent technology, and further introduces the functions of agents in the model as well as the process of antibody selection.

A Research into an Intrusion Detection System Based on Immune Principle and Multi-Agent in WSN

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