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1. Determine the benefits of data mining to the businesses when employing:
a. Predictive analytics to understand the behavior of customers
b. Associations discovery in products sold to customers
c. Web mining to discover business intelligence from Web customers
d. Clustering to find related customer information
2. Assess the reliability of the data mining algorithms. Decide if they can be trusted and predict the errors they are likely to produce.
3. Analyze privacy concerns raised by the collection of personal data for mining purposes.
a. Choose and describe three (3) concerns raised by consumers.
b. Decide if each of these concerns is valid and explain your decision for each.
c. Describe how each concern is being allayed.
4. Provide at least three (3) examples where businesses have used predictive analysis to gain a competitive advantage and evaluate the effectiveness of each businesss strategy.

(a). Using predictive analytics to comprehend customer behaviors: To remain competitive within the market, sellers need to comprehend present consumer behavior as well as predict those of the future. The accurate prediction of these behaviors and the full understanding of customer behaviors can help them in improving sales, retaining customers, and extending the relationships sustained with customers. This is realized through predictive analysis data mining, which offers the users, impactful insights throughout the organization (Greene, 2012). Predictive analytics is where statistics and mathematics integrate to business and marketing to establish patterns in data and extrapolating the patterns to future business cases and issues, so as to reduce costs, improve response rates, increase the efficiency of processes and consequently boost revenue levels. Data mining has different components, but the most significant is defining the problem, evaluating the available data and developing predictive models.

(b). Associations discovery for the commodities sold to consumers helps the retailer or other business to capture the unique identifier of a given product. Through 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 used to support business-related strategies and applications like inventory management, marketing promotions and customer relations management.
(c). Mining information on web 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 face 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 depict more value. For example, through 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 are 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. Through establishing the highest inter-cluster similarity, the characteristics of the members are used 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.

The reliability of data mining algorithms can be done through 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 space 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 through the application of tests to determine whether a given point belongs to the cluster in question. In such a case, a reliable algorithm will detect outliers and noise, and eliminate their negative effects or delete them entirely. Through this process, the testing of the validity of the algorithm can be done while the process of clustering is ongoing, or it can be done at the post-processing phase.
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