Data Mining Essay Example | Topics and Well Written Essays - 1000 words

Data Mining - Essay ExampleData mine has variant components, but the most significant is defining the problem, evaluating the available info and developing predictive models. (b). Associations discovery for the commodities sold to consumers dos the retailer or early(a) business to capture the unique identifier of a given product. by capturing this information, the seller is able to analyze the data, so that they can learn the purchasing behavior of their customers. The information derived is use to have business-related strategies and applications like inventory management, marketing promotions and customer relations management. (c). Mining information on clear usage is very important to the effective management of websites, planning the development of adaptive websites, administering business and support services, increasing personalization as well as analyzing the flow of network traffic. Further, fast business growth of businesses forces businesses and customers to baptis mal font a different situation, where competition plays a major role in determining the strategies adopted by businesses (Greene, 2012). On the other hand, the customer is exposed to more options to choose from, therefore, will need to follow the businesses that submit more value. For example, finished discovering that many customers of a given business come from teen customers, may help the company to adjust their targeting outlook, to ensure that it targets the focus group better. (d). Clustering analysis traces groups of data entities or objects that atomic number 18 similar in certain aspects. The members of the different groups are supposed to be more similar to other members, and different from the members of other clusters. The target of clustering is the discovery of high-quality groups, where inter-cluster similarity is lowest but intra-cluster similarity is highest. by means of establishing the highest inter-cluster similarity, the characteristics of the members are us ed or viewed as the customer information that can be tracked or targeted to increase the impact of the business, among the given high-quality cluster. 2. The reliability of data mining algorithms can be done finished the validation of data mining modes. The process involves the assessment of the performance of the mining models against real data. This is done through understanding the characteristics and the quality of the algorithms before deploying them into the production environment (Chung, & Gray, 1999). To determine the reliability of data mining algorithms, the deployment of different statistical validity measures is checked, towards determining whether there are issues in the model or the data. The reliability of data mining algorithms is determined through the scalability of the clustering techniques. This is particularly true, in the case of large data sets, where spot and speed are high. For example in the case that the algorithm in the case of a database that contains millions of records, shows linear or close to linear time complexity, which demonstrates that the reliability of the algorithm is high. The reliability of the algorithm can be determined throug