An Introduction to Data Mining Overview Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. The automated, prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools typical of decision support systems. Data mining tools can answer business questions that traditionally were too time consuming to resolve. They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations. Most companies already collect and refine massive quantities of data. Data mining techniques can be implemented rapidly on existing software and hardware platforms to enhance the value of existing information resources, and can be integrated with new products and systems as they are brought on-line. When implemented on high performance client/server or parallel processing computers, data mining tools can analyze massive databases to deliver answers to questions such as, "Which clients are most likely to respond to my next promotional mailing, and why?" This paper provides an introduction to the basic technologies of data mining. Examples of profitable applications illustrate its relevance to today's business environment as well as a basic description of how data warehouse architectures can evolve to deliver the value of data mining to end users. The Foundations of Data Mining 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: Massive data collection Powerful multiprocessor computers Data mining algorithms Commercial databases are growing at unprecedented rates. A recent META Group survey of data warehouse projects found that 19% of respondents are beyond the 50 gigabyte level, while 59% expect to be there by second quarter of 1996.1 In some industries, such as retail, these numbers can be much larger. The accompanying need for improved computational engines can now be met in a cost-effective manner with parallel multiprocessor computer technology.
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.
Data mining is continuingly growing in use throughout many different industries for a variety of business purposes. Some of these industries are financial and banking, healthcare, retail, etc. (Groth, 2000). One of the main uses of data mining is marketing a company’s products and or services to customers (Groth, 2000).
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.
Every interaction your company has with a customer or supplier likely generates a data trail and this data provides a wealth of information for marketers. Extracting that information and getting it into usable shape requires sophisticated data mining tools. One example of this technology is the used by police departments to identify patterns in crime. We will define, explain and discuss main aspects of data mining. Also its benefits and negative issues.
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...
In today’s society, technology has become more advanced than the human’s mind. Companies want to make sure that their information systems stay up-to-date with the rapidly growing technology. It is very important to senior-level executives and board of directions of companies that their systems can produce the right and best information for their company to result in a greater outcome and new organizational capabilities. Big data and data analytics are one of those important factors that contribute to a successful company and their updated software and information 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
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.
Ans: The several applications required mining transaction data to capture the customer’s behavior. The efficiency of data mining is more important factor than requirement of accuracy of the result. As the size of database increase now a days very fastly, its can be an effective approach to data mining.
Description: Data Mining contains of several algorithms that fall into four different categories(Shobana et al. 2015)
However, a DSS tool is Online Analytical Processing (OLAP) –Decision support system is an interactive computerized system which gathers and presents for business purposes from various sources (webopedia.com, 2014). OLAP is a tool that enables the user to analyze different data dimensions. It provides time series as well as trend analysis views. OLAP tools are used by analysts where they employ relatively simple techniques which include induction, deduction as well as pattern recognition to so as to derive new information as well as insights. OLAP is also used in data mining using OLAP server which sits between a database management systems and a client. For example, Infosys – an information technology consultancy, recommended one of the clients to use OLAP solutions as a supply chain analytic solution which contributed 30% of its gross revenue.
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.
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.
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.
Big data processing is recently becoming growingly salient in modern age due to the constant growth of data generated by diverse fields. However the effectiveness of discovering patterns for knowledge discovery is unclear. Knowledge Discovery concepts play a major role in data analysis.In the overall scheme of knowledge discovery, Data Mining techniques has been found unexpectedly to usually devoted to extraction of information from structured databases and data warehouse.Text Data Mining techniques, on the other hand, are dedicated to extraction of information from unstructured textual data e.g electronic texts on web. Although there are various researches being carried out in the area of conventional data mining, little has been carried