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importance of e-commerce in modern business
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E-commerce has altered the face of most business purposes in modest enterprises. Internet technologies have impeccably automated interface methods between consumers and venders, retailers and wholesalers, distributors and sweatshops, and factories and their numerous suppliers. In general e-commerce and e-¬business. Have permitted on- en easier. With data relating to various views of business online communications. Also, producing large-scale real-time data has never bens being eagerly available; it is only appropriate to seek the services of data mining to make (business) sense out of these data sets.
Data mining (DM) has as its foremost goal, the age band of non-obvious yet useful info for decision makers from very large databases. The numerous mechanisms of this generation include generalizations, accumulations, summarizations, and categorizations of data. These forms, in turn, are the result of applying erudite modeling techniques from the diverse fields of indicators, artificial intelligence, and database organization and computer graphics.
5.2 THE ATTAINMENT OF A DATA MINING...
Have you ever purchased any product on the Internet, used the Internet to collect information or data, or played computer games on the Internet? You must agree that it is fast, easy, and enjoyable. The Internet has been a part of our daily life for several years now. In addition, in the business world, a new business model, E-business and E-commerce, has appeared for several years. According to Ali, there are two main types of E-commerce: B2B and B2C (2000). One is business to business (B2B). This means that enterprises use the Internet to transact or trade between business operations and their partners. Another is business to consumer (B2C). In other words, enterprises provide products, support good, and services to the customers on the Internet.
Mining of data streams is required to be formalized within a theory of data stream computation. This formalization would facilitate the design and development of algorithms based on a concrete mathematical foundation. Approximation techniques and statistical learning theory represent the potential basis for such a theory. Approximation techniques could provide the solution, and using statistical learning theory would provide the loss function of the mining problem. The above issues represent the grand challenges to the data mining community in this essential field. There is a real need inspired by the potential applications in astronomy and scientific laboratories as well as business applications to address the above research problems.
"Data mining is the process of discovering meaningful new correlations, patterns, and trends by sifting through large amounts of data stored in repositories, using pattern recognition technologies as well as statistical and mathematical techniques" (SPSS). However, really data mining turns databases into knowledge bases which is one of the fundamental components of expert systems. Instead of the computer just blindly pulling data from a database, the compu...
Customer data mining has a vast potential, but the inner workings of this business practice are quite complex. According to Jason Frand, a Managerial Computing and Information Systems Professor at UCLA, customer data mining is a very complicated process that requires experts with a high level of understanding. Several types of analytical softwares are available for customer data mining, with the two main softwares being statistical software and machine learning software, which enables the computer to ‘learn’ from data. These softwares seek four main relationships.The first relationship sought are classes which are information stored in predeter...
According to Gundecha and Liu (2012), the major aims of a data mining process include manipulating large-scale data and deciphering actionable patterns in them.
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 are various kinds of definitions about what data mining is. The authors in [1] define data mining as “the process of extracting previously unknown information from (usually large quantities of) data, which can, in the right context, lead to knowledge”. Data mining is widely used in areas such as business analysis, bioinformatics analysis, medical analysis, etc. Data mining techniques bring us a lot of benefits. Business companies can use data mining tools to search potential customers and increase their profits; medical diagnosis can use data mining to predict potential disease. Although the term “data mining” itself is neutral and has no ethical implications, it is often related to the analysis of information associated with individuals. “The ethical dilemmas arise when data mining is executed over the data of an individual” [2]. For example, using a user’s data to do data mining and classifying the user into some group may result in a variety of ethical issues. In this paper, we deal with two kinds of ethical issues caused by data mining techniques: informational privacy issues in web-data mining and database security issues in data mining. We also look at these ethical issues in a societal level and a global level.
Data mining is also called knowledge discovery in database. In computer science, the process of discovering knowledge and relationship in large amount of data. This field combines from statistics and artificial intelligence with database management known as data set.
Today, the topic of data mining has much interest in government, business, and research circles. With the growth of computer use within these areas has also come a greater desire to let the computers do the work that used to be done by humans. The problem, nowadays, is that the data that needs to be analyzed has become too large and cumbersome for one person or even teams of people to envision tackling without help from computers. These computers are no longer mere crunchers of numbers but now they find the patterns that the humans used to find. From this growth has arisen a vast body of knowledge concerned with this process of data analysis. As with much other information, the Internet is employed to make available the ever-growing body of information on this topic. Many general sources of information [a,b,c] are now online. These are updated and expanded upon almost a constant basis. The use of the Internet to disseminate and collect information is itself a consideration in this field. The amount of information is expanding at such a rate that old methods of information disposal, such as paper journals and b...
HAND, D. J., MANNILA, H., & SMYTH, P. (2001).Principles of data mining. Cambridge, Mass, MIT Press.
Without data mining technology companies find little value in raw data gathered from customers despite investing in databases and processors to house it. Accumulated data can reflect transactions, customer contacts, descriptions, how they have reacted to past marketing, and more (Gupta & Aggarwal, 2012). Doug Alexander (n.d.), a Professor at the University of Texas, calls this being “data rich, information poor”. Data mining software can convert this otherwise useless data into valuable information that benefits the entire company.
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 mining, or knowledge discovery, is the computer-driven process of searching through and analysing enormous data and then understanding the meaning of the data. Data mining helps predict future trends which allow businesses to make
E- Commerce is a phenomena that is emerging rapidly between businesses all over the world, and it has affected the businesses at all sizes in many aspects.
The high take-up of the Internet leads to variety of opportunities in front of companies. People are more online than ever. They spend many hours each day on Social Networks such as Facebook and Google+. It is no wonder that buying and selling can now be done in a more convenient way. Although traditional shopping is still thriving, online shopping can be an alternative for people wanting to save time and money. If a certain customer plan to go shopping, it could be stressful and also be time consuming. E-business has made shopping or any kind of transactions online much easier and convenient. It introduces new facilities, opportunities and way of shopping for both vendors and customers.