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Data mining ethics
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Knowledge Discovery in Databases Abstract Knowledge Discovery in Databases is the process of searching for hidden knowledge in the massive amounts of data that we are technically capable of generating and storing. Data, in its raw form, is simply a collection of elements, from which little knowledge can be gleaned. With the development of data discovery techniques the value of the data is significantly improved. A variety of methods are available to assist in extracting patterns that when interpreted provide valuable, possibly previously unknown, insight into the stored data. This information can be predictive or descriptive in nature. Data mining, the pattern extraction phase of KDD, can take on many forms, the choice dependent on the desired results. KDD is a multi-step process that facilitates the conversion of data to useful information. Our increased ability to gain information from stored data raises the ethical dilemma of how the information should be treated and safeguarded. Introduction The desire and need for information has led to the development of systems and equipment that can generate and collect massive amounts of data. Many fields, especially those involved in decision making, are participants in the information acquisition game. Examples include: finance, banking, retail sales, manufacturing, monitoring and diagnosis, health care, marketing and science data acquisition. Advances in storage capacity and digital data gathering equipment such as scanners, has made it possible to generate massive datasets, sometimes called data warehouses, that measure in terabytes. For example, NASA's Earth Observing System is expected to return data at rates of several gigabytes per hour by the end of the century. Mod... ... middle of paper ... ... of data warehouses increase. New methods of analysis and pattern extraction are being developed and adapted to KDD. Which method is used depends on the domain and results expected. The accuracy of the recorded data must not be overlooked during the KDD process. Domain specific knowledge assists with the subjective analysis of KDD results. Much attention has been given to the data mining phase of KDD but earlier steps, such as data cleaning, play a significant role in the validity of the results. The potential benefits of discovery driven data mining techniques in extracting valuable information from large complex databases are unlimited. Successful applications are surfacing in industries and areas were data retrieval is outpacing man's ability to effectively analyze its content. Users must be aware of the potential moral conflicts to using sensitive information.
Traditional business intelligence tools are being replaced by data discovery software. The data discovery software has numerous capabilities that are dominating purchase requirements for larger distribution. A challenge remaining is the ability to meet the dual demands of enterprise IT and business users.
One of the biggest problems that affect everyone is data aggregation. The more the technology develop, the powerful and dangerous it gets. Today there are many companies that aggregate a lot of information about us. Those companies gathering our data from different sources, which create a detailed record about us. Since all services have been computerized whether it is handled directly or indirectly through computers, there is no way to hide your information. We used computers, because they are faster, better, and accurate more that any human being. It solved many problems; however, it created new ones. Data does not means anything if it stands alone, because it is only recoded facts and figure, yet when it organized and sorted, it become information. These transformed information. Data aggregation raises many questions such as, who is benefiting from data aggregation? What is the impact on us (the users)? In this paper I will discuses data aggregation and the ethics and legal issues that affect us.
Although data mining is a relatively new term, the technology is not. Companies have used powerful computers to sift through volumes of supermarket scanner data and analyze market research reports for years. However, continuous innovations in computer processing power, disk storage, and statistical software are dramatically increasing the accuracy of analysis while driving down the cost.
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.
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
[7] Elmasri & Navathe. Fundamentals of database systems, 4th edition. Addison-Wesley, Redwood City, CA. 2004.
n data mining, trends and patterns are identified on a huge set of data to discover knowledge. In such analysis, varieties of algorithms exist for extracting knowledge such as clustering, classification and association rule mining. Thus, association rules mining one domain for delivering knowledge on complex data. Moreover, the basis of the discovered association rules is usually determined by the minimum support s % and minimum confidence c% to represent the transactional items in database D. Thus, it has the implication of the form AB, where A is the antecedent and B is the consequent. The problem with such display of rules is the disclosure of sensitive information to the external part when data is shared. Hence Privacy Preserving in Data Mining (PPDM) related to Association Rules emerges.
A data stream is a real time, continuous, structured sequence of data items. Mining data stream is the process of extracting knowledge from continuous, rapid data records. Data arrives faster, so it is a very difficult task to mine that data. Stream mining algorithms typically need to be designed so that the algorithm works with one pass of the data. Data streams are a computational challenge to data mining problems because of the additional algorithmic constraints created by the large volume of data. In addition, the problem of temporal locality leads to a number of unique mining challenges in the data stream case. The data mining techniques namely clustering, classification and frequent pattern mining are applied to extract the knowledge from the data streams. This research work mainly concentrates on how to find the valuable items found in a transactional data of a data stream. In the literature, most of the researchers have discussed about how the frequent items are mined from the data streams. This research work helps to find the valuable items in a transactional data. This is a new research idea in the area of data stream frequent pattern mining. Frequent Item mining is defined as finding the items which are occurring frequently and above the given threshold. Valuable item is nothing but finding the costliest item or most valuable items in a data base. Predicting this information helps businesses to know about the sales details about the valuable items which guide to make important decisions, such as catalogue drawing, cross marketing, consumer shopping and performance scrutiny. In this research work, two new algorithms namely VIM (Valuable Item Mining) and TVIM (Tree based Valuable Item Mining) are proposed for finding the...
A data warehouse comprised of disparate data sources enables the “single version of truth” through shared data repositories and standards and also provides access to the data that will expand frequency and depth of data analysis. Due to these reasons, data warehouse is the foundation for business intelligence.
Description: Data Mining contains of several algorithms that fall into four different categories(Shobana et al. 2015)
In the past, the term Data Mining was, and still is, used to designate the activity of pulling useful information from databases. Now, this term is recognized to apply but to one activity in a very large process to extract knowledge from opaque databases. The overall process is known as Knowledge Discovery in Databases, (KDD). This process is comprised of many subprocesses which when linked together provide a firm foundation for knowledge acquisition from large databases. Many tools, techniques, and disciplines come together under the umbrella of KDD.
For the past couple of decades the majority of businesses have wanted to construct a data-driven organization or company. Furthermore, companies around the world are considering harnessing data as a basis of competitive advantage over other companies. As a result, business intelligence and data science use are popular in many organizations today. The increase in adoption of these data systems is in response to the heavy rise in communications abilities the world over. Which, in turn ,has increased the need for data products. Indeed, the Data Scientist profession is emerging to be one of the better-paying professions due to the urgent need of their labor. This paper is going to discuss what business intelligence is all about and explain data science that is usually confused to be similar to business intelligence. I will tackle a brief overview of data scientists and their role in organizations.
...puter technology are rooted in the general ethical issues that people in society deal with. For example, the ethical issues such as invasion of privacy, theft, and fraud have been around since human beings began interacting with each other. The fact is that elements of these ethical issues are not unique to the computer field or computer technology. These current technologies raise the same ethical dilemmas with conditions that are unique to computer and cyber technology. This explains why we general ethical issue are such as privacy, theft and fraud are reexamined as informational privacy, identity theft and computer fraud in computer technology.
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.
Data privacy issues arise in wide range of areas such as healthcare records, financial information, regarding genetic material in biology, geographical records, criminal justice and investigations and also in the use of