Market Basket Analysis Essay

typical example is market basket analysis, which analyzes purchasing habits of customers by finding associations between different items in customers―shopping baskets.

2) Classification and prediction is the process of identifying a set of common features and models that describe and distinguish data classes or concepts. The models are used to predict the class of objects whose class label is unknown. A bank, for example, may classify a loan application as either a fraud or a potential business using models based on characteristics of the applicant. A large number of classification models have been developed for predicting future trends of stock market indices and foreign exchange rates.

3) Clustering analysis segments a large set of data into subsets or clusters. Each cluster is a collection of data objects that are similar to one another within the same cluster but dissimilar to objects in other clusters. In other words, objects are clustered based on the principle of maximizing the intra-class similarity while minimizing the inter-class similarity. For example, clustering techniques can be used to identify stable dependencies for risk management and investment management.

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The quantity of data collected continues to expand rapidly, especially due to the increasing ease, availability, and popularity of business conducted on the internet, or e-commerce. Retail data mining can help identify customer buying behaviours, discover customer shopping patterns and trends, improve the quality of customer service, achieve better customer retention and satisfaction, enhance goods consumption ratios, design more effective goods transportation and distribution policies, and reduce the cost of business. A few examples of data mining in the retail industry are outlined as