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Data Mining Essay

1595 words - 7 pages

In today’s society, businesses face many challenging factors that not only affect their products and services but also their initiatives to sustain and increase the company’s profit. Factors such as product line, profit margins, marketing approaches, and resources play key roles into the overall success and longevity of an organization. Most importantly, the foundation and key value to an organization is the target audience that will invest in the organization’s product and services for which many organizations hope to not only retain previous customers but also attract prospective customers. For that reason, it is imperative that an organization collect pertinent information regarding who ...view middle of the document...

Prior to 1990, data mining was referred to as “Knowledge Discovery and Data Mining (KDD)”, for which both terms are used interchangeably today. According to researchers at IBM, “KDD is an interdisciplinary area focusing upon methodologies for extracting useful knowledge from data” (Placeholder4). Primitively, data was collected and stored on computers, tapes and disks in the 1960s. Although this was a better method to archive information, data could not be manipulated until the next evolutionary step happened in the 1980s with the introduction of relational databases and structured query languages. In 1989, the first International Joint Conference on Artificial Intelligence (IJCAI) on Knowledge Discovery on Databases was held to establish rules and regulations for the new practice. The evolution continued in the 1990s with data warehousing becoming a new common process allowing organizations to store their customer data into servers located in data centers. From 1991 to 1994, workshops on KDD discussed the advances in knowledge that would take place and how data mining would affect customers and the businesses that used the process. Data mining was introduced and became widespread throughout the new millennium for which large corporations such as Walmart and Target became to invest in the initiatives to better market products to customers in specific regions. Today, data mining now helps many businesses forecast projected sales based on vigilant data analysis. There are three different areas that contribute to the continuous growth of data mining; statistics, artificial intelligence, and machine learning. Statistics are the building blocks of advanced data mining techniques. Many decisions and approaches are based upon the traditional statistical methods programmed into complex data mining applications. Artificial intelligence is a resource that attempts to simulate the human thought process or human intelligence in statistical problems. This technology allows applications to be able to make conclusions from the data that is contained in the application. The third area of data mining is machine learning. Machine learning is a complicated mechanism used to combine the concepts of both statistics and artificial intelligence to create an advanced technology that will aid prominent business leaders in progression of their corporation (Placeholder4).
Data Mining has techniques have been prevalent for many years and now that companies are storing large amounts of data on databases, it has become even more widespread. There are many techniques used by enterprise corporations to analyze data for which many do not share actual terms. Although terminology may be different among organizations, there are several practices used by analysts that make up the data mining process, including association, classification, clustering, prediction, sequential patterns, and decision trees. Association is the most common and direct technique. This method makes a...

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