You will prepare and submit a term paper on Data Mining. Your paper should be a minimum of 1000 words in length. The more the business understands why some of its customers are loyal and how it can continue to retain and attract customers from different segments, the more the business will be able to come up with compelling offers and messages that are relevant. Predictive analytics results in an analytical framework which helps in the prediction of product preferences and customer buying habits required in the discovery of meaningful relationships and patterns in the customer data so as to accomplish better market targets and drive customer loyalty and value (Turkey, 1997). Association discovery can be employed in a business to determine the affinity patterns of its products. This information is important in optimizing the manner in which the business orders are picked and accumulated from their centers of distribution. The same information obtained through association discovery can be based on to generate dynamic rules which would permit business orders to be picked or accumulated taking into consideration the chances of another order that is identical occurring in a span of few days. The outcome of such an optimization is that decisions take less time thus substantially saving on the business costs (Agresti, 2002). Web mining allows the business to sift through information regarding the market situation so as to identify where the market value is. Such discoveries will help the business come up with new opportunities as the business with such information will have the ability to implement parallel processing systems and high performances as it is in a position to analyze large data in a very short time. The business can also make use of data mining to try out different business models to best understand the market situation and adapt to it. Such information also helps the business in making better predictions (Witten and Eibe, 2011). The clustering of information using data mining related to customers have served as important networks to business in the process of handling numerical market data. The market is normally the main target for any business when it comes to information clustering. This data mining technique helps the business segment potential customers on the basis of given sets of attributes. The technique also helps businesses to adapt the best operation methods through discovering market facts that are hidden giving the business an added advantage when it comes to its competitive position. The business will also be in a position to understand better what their customers really need and want. 2. Assess the reliability of the data mining algorithms. Decide if they can be trusted and predict the errors they are likely to produce Data mining algorithms is one of the main methods used in the exploration of linkages and relationships among sets of data. Such algorithms have been relied on by many businesses as they are believed to have the ability of reducing computational efforts and are in a position to deal with structural system’s failure mode. Despite data mining algorithms being viewed as being highly accurate and efficient in their operation, the algorithm has been associated with a number of identification, exploration and exploitation errors. Reliability is seen in the way any given data mining model operates on different sets of data.