Data mining is a field that is a combination of numerous other fields such as the database research, artificial intelligence and statistics. Data mining involves looking for patterns in vast amounts of data as a part of knowledge discovery process. (Huang, Joshua Zhexue, Cao, Longbing, Srivastava, Jaideep, 2011) contains numerous papers that are solely dedicated to discussing the advancements that have been made in the field of data mining and knowledge discovery. A lot of people have performed a thorough research on all that has been done in data mining and the future possibilities that are soon to be implemented practically. The research not only covers the history and the reasons that led to various advancements being made but they also cover the detail models of the proposed solutions to deficiencies in existing systems. An extension of the data mining process is the distributed data mining. It is a process that is not only being used widely but it also has been researched upon and studied quite a lot in previous years. (Bin Liu, Shu-Gui Cao, Xiao-Li Jia, Zhao- Hua Zhi, July 2010) analyzes how the methods and techniques used for the traditional data mining process need to be developed further to make them suitable for the distributed data mining environments. The three main types of the distributed data mining systems are identified and then discussed in detail. The systems are categorized on the infrastructure they are built on; the meta-learning, grid and data mining agents. The three classes are examined in detail, listing their shortcomings and benefits over each other. The main problems regarding all three classes are also discussed and the related models for knowledge discovery are proposed which can be used to overcome ... ... middle of paper ... ...sed for knowledge discovery, namely Upgrading and Rerouting. The process used involves identifying each resource as a component of a certain category based on the provided technical specifications. The name given to this process in Virtual Organization (VO) and it needs only limited information about the resources to make the decision. There are more than one VO’s and each handles different resources and share information about the resources controlled by each. When a request is made it is discovered which resource must be most suitable for the request and then the request is forwarded to the VO that is responsible for handling the said resource. The requesting process also enables the VO’s to update their knowledge of the available Grid resources using the discovery mechanism. The performance of both discover mechanisms, Upgrading and Rerouting are also discussed.
Privacy Preserving Data Mining (PPDM) was proposed by D. Agrawal and C. C. Agrawal [1] and by Y. Lindell and B. Pinkas [5] simultaneously. To address this problem, researchers have since proposed various solutions that fall into two broad categories based on the level of privacy protection they provide. The first category of the Secure Multiparty Computation (SMC) approach provides the strongest level of privacy; it enables mutually distrustful entities to mine their collective data without revealing anything except for what can be inferred from an entity’s own input and the output of the mining operation alone by Y. Lindell and B. Pinkas in [5], J. Vaidya and C.W.Clifton in [6]. In principle, any data mining algorithm can be implemented by using generic algorithms of SMC by O.Goldreich in [7].However, these algorithms are extraordinarily expensive in practice, and impractical for real use. To avoid the high computational cost, various solutions those are more efficient than generic SMC algorithms have been proposed for specific mining tasks. Solutions to build decision trees over the horizontally partitioned data were proposed by Y. Lindell and B. Pinkas in [5]. For vertically partitioned data, algorithms have been proposed to address the association rule mining by J. Vaidya and C.W.Clifton in [6], k-means clustering by J. Vaidya and C. Clifton in[8], and frequent pattern mining problems by A.W.C. Fu, R.C.W. Wong, and K. Wang in [9]. The work of by B. Bhattacharjee, N. Abe, K. Goldman, B. Zadrozny, V.R. Chillakuru, M.del Carpio, and C. Apte in [10] uses a secure coprocessor for privacy preserving collaborative data mining and analysis. The second category of the partial information hiding approach trades pr...
The concept of Multiple Intelligences has come out after Howard Gardner redefined intelligence. According to Gardner’s theory, human beings have different types of intelligence and based on their respective intelligence types, human beings have different skills in different areas. Human beings can be more successful and productive on areas they are skillful. Therefore, choosing appropriate professions, in accordance with one’s intelligence type, is important for individuals as well as for the society. This study aims to develop a technique to help high school students in the profession selection process using artificial intelligence. Questionnaire has been utilized as the research method and the data have been analyzed via Fuzzy Logic Toolbox which is subunit of the software called MATLAB. It was aimed to direct students to appropriate profession and build more successful and productive professions.
Joseph P. Bigus. 1996. Data Mining with Neural Networks: Solving Business Problems from Application Development to Decision Support. McGraw-Hill, Inc., Hightstown, NJ, USA.
Data mining has emerged as an important method to discover useful information, hidden patterns or rules from different types of datasets. Association rule mining is one of the dominating data mining technologies. Association rule mining is a process for finding associations or relations between data items or attributes in large datasets. Association rule is one of the most popular techniques and an important research issue in the area of data mining and knowledge discovery for many different purposes such as data analysis, decision support, patterns or correlations discovery on different types of datasets. Association rule mining has been proven to be a successful technique for extracting useful information from large datasets. Various algorithms or models were developed many of which have been applied in various application domains that include telecommunication networks, market analysis, risk management, inventory control and many others
There is a debate between the benefits and potential informational privacy issues in web-data mining. There are large amount of valuable data on the web, and those data can be retrieved easily by using search engine. When web-data mining techniques are applied on these data, we can get a large number of benefits. Web-data mining techniques are appealing to business companies for several reasons [1]. For example, if a company wants to expand its bu...
R. Agralwal, T.Imielinski, and A.Swami. Mining associations between sets of items in large databases. In P.Buneman and S. Jajodia, editors, SIGMOD93, pages 207-216, Washington, D.C, USA, May 1993
Due to the development of ICT, adaptive learning, which takes into account individual learners’ needs, is changing. Learners’ learning styles are one of the most significant characteristics. They can be categorized according to a number of criteria which are based on cognitive and emotional components of personality. Their combination leads to the countless individual variants of real learning methods which – to a certain degree – can be influenced by the current e-learning resources. When the e-learning resources can react to the learners’ input characteristics or their learning results, they become adaptive e-learning systems (AES) or intelligent AES.
...fman R. A. - "Data Mining and Knowledge Discovery" - A Review of issues and Multi- strategy Approach". Reports of the Machine Learning and Inference Laboratory, MCI 97-2, George Mason University, Fairfax, V.A. 1997. http://www.mli.gmu.edu/~kaufman/97-1.ps
Data mining techniques are the result of a long process of research and product development. This evolution began when business data was first stored on computers, continued with improvements in data access, and more recently, generated technologies that allow users to navigate through their data in real time. Data mining takes this evolutionary process beyond retrospective data access and navigation to prospective and proactive information delivery. Data mining is ready for application in the business community because it is supported by three technologies that are now sufficiently mature:
HAND, D. J., MANNILA, H., & SMYTH, P. (2001).Principles of data mining. Cambridge, Mass, MIT Press.
Sentiment analysis, also called as opinion mining, is the field of study that analyzes people’s opinions, sentiments, evaluations, appraisals, attitudes and emotion towards entities such as products, services or organizations, individuals, issues, topics and their attributes. Sentiment analysis and opinion mining mainly focuses on opinions which express or imply positive, negative or neutral sentiments. Due to the big diversity and size of social media there is a need of automated and real time opinion extraction and mining. Mining online opinion is a form of sentiment analysis that is treated as a difficult text classification task.
There are many different types of students. All students have their own way of studying and learning material. A student’s attitude is the most determining factor in how well a student performs academically. Some students are eager to learn and try their best; however, some students could care less about learning. Each year students decide whether they will succeed or fail in school. All students fall into one category or another. Students can be classified into three categories: Overachievers, Average Joes, and Do Not Give a Rips.
The dynamics of our society bring many challenges and opportunities to the business world. Within the last decade, hundreds of jobs have emerged particularly in the technology sector to help keep up with the ever-changing world and to compete on a larger and better scale than the competition. Two key job markets and the basis of this research paper are business intelligence or BI and data mining or DM. These two fields play a very important role in small to large companies and are becoming higher desired sectors within the back offices of the workplace. This paper will explore what the meaning of BI and DM really is, how they are used and what we can expect as workers and learners of the technology and business fields for the future.
Information privacy, or data privacy is the relationship between distribution of data, technology, the public expectation of privacy, and the legal and political issues surrounding them.
The data mining process will use the mapping function which involved the decision tree and also the neural network to develop. It needs the web server and the database server to be constructed in an operating database to record the browsing route of the users. The data mining will use to identify the user’s information and classify them into different classes using decision tree.