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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...
To make the best of the seemingly untappable resource, a new field of data extraction, visualization, management and manipulation has come about – Data Analytics or Data Science. People who indulge in this data mining
Data mining has many benefits. Stores are able to stock merchandise that better reflects what customers want. When Victoria’s Secret started tracking user purchases they noticed that customers in Miami bought much more white lingerie than customers in other areas. As a result they began stocking more white products instead of uniformly stocking all stores benefiting both the store and the customer[i]. Another benefit is that it allows companies to consolidate data from many different sources so that more time can be spent analyzing data than finding it in the first place. This is useful for companies that have multiple financial systems and spend a lot of time trying to combine data into a more useful format rather than doing the actual analysis of the data. A more dramatic example is that some say that 9/11 could have been prevented if the FBI had better data mining tools to share and combine information from different offices[ii]. In addition to crime prevention and financial analysis the medical research community can use these techniques in medical research to identify trends and causes of disease.
After understanding the possible outcomes and usages of Big Data Mining and Analytics, the study of the process is necessary to identify the real possibilities behind this techniques and how this can improve a business performance. To do this; we should comprehend the basics about data mining and the process that leads from pure data to insights.
In general, nearly every time you surf or make a purchase online, information is collected on your actions. Then targeted advertising can be presented online, emailed, snail-mailed or even phoned to you. The business concept behind this is “best predictor of future behavior is relevant past behavior” (ala Dr. Phil). One company claims that retailers can increase their return on data mining investment by 1,000 percent.[i] The first step in understanding data mining is to look at the various ways t...
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.
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...
HAND, D. J., MANNILA, H., & SMYTH, P. (2001).Principles of data mining. Cambridge, Mass, MIT Press.
The Information revolution is changing our daily lives. With the rapid development of computers and the internet, online commerce has become quite common and plays an important role in the modern world. Online business has been booming in recent years. US online retail sales rose an average of 11% in the first three months of 2009 (“US Online Sales Up,” 2009). The growth of online sales may be due to the growing number of consumers who shop online.
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.
Electronic Commerce as popularly as E-commerce has become a big deal in our growing economy due to the increase use of online systems. E-commerce now of the fastest growing business in the world. The technology has change the way of business. Business that have physical location have now made it an effort to focus their online business. It is the new sort of business platform where you can make use of different technologies like electronic data interchange or transfer document electronically. Online business is an effective of sales.
Humans can expand their knowledge to adapt the changing environment. To do that they must “learn”. Learning can be simply defined as the acquisition of knowledge or skills through study, experience, or being taught. Although learning is an easy task for most of the people, to acquire new knowledge or skills from data is too hard and complicated for machines. Moreover, the intelligence level of a machine is directly relevant to its learning capability. The study of machine learning tries to deal with this complicated task. In other words, machine learning is the branch of artificial intelligence that tries to find an answer to this question: how to make computer learn?
E-commerce is about two decades old, yet due to its fascinating dimensions, it remains a challenging area for researchers and professionals.
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.