The CRISP-DM Case Study
The CRISP-DM is developed and introduced by IBM SPSS Modeler. It helps to organize task streams, output, and annotations according to the phases of a usual data mining project. CRISP-DM IBM SPSS Modeler consists of six difference phases which are business understanding, data understanding, data preparation, modeling, evaluation and deployment as shown in Figure 1 in order to find the patterns and discovered the knowledge. The CRISP-DM model is flexible and can be customized easily. The key steps in the CRISP-DM process are as follows:
• Business Understanding involves determining and defining business objectives in business terms, translating these to data mining goals and making a project assessment and plan. It involves many people in the …show more content…
Different kinds of methods and techniques are needed to find different kinds of patterns. There are two classes of data mining taxonomy which are predictive and descriptive (Akkaya & Uzar, 2011). The data mining tasks that are most apply, use and popular are classification, association, clustering, trend analysis and regression (Fu, 1997; Gheware et al., 2014).
• Classification is the derivation of a function or model which determines the class of an object based on its attributes. A set of objects is given as the training set in which every object is represented by a vector of attributes along with its class. The examples of classification model can be used to diagnose a new patient’s disease based on the patient’s diagnostic data such as age, sex, weight, temperature and blood pressure.
• Association is the discovery of togetherness or connection of objects also termed as association rule. An association rule reveals the associative relationships among objects which are useful for marketing and advertising. The example of association is a retail store may discover that people tend to buy soft drinks together with potato chips and then put the potato chips on sale to promote the sale of soft
• Business Understanding involves determining and defining business objectives in business terms, translating these to data mining goals and making a project assessment and plan. It involves many people in the …show more content…
Different kinds of methods and techniques are needed to find different kinds of patterns. There are two classes of data mining taxonomy which are predictive and descriptive (Akkaya & Uzar, 2011). The data mining tasks that are most apply, use and popular are classification, association, clustering, trend analysis and regression (Fu, 1997; Gheware et al., 2014).
• Classification is the derivation of a function or model which determines the class of an object based on its attributes. A set of objects is given as the training set in which every object is represented by a vector of attributes along with its class. The examples of classification model can be used to diagnose a new patient’s disease based on the patient’s diagnostic data such as age, sex, weight, temperature and blood pressure.
• Association is the discovery of togetherness or connection of objects also termed as association rule. An association rule reveals the associative relationships among objects which are useful for marketing and advertising. The example of association is a retail store may discover that people tend to buy soft drinks together with potato chips and then put the potato chips on sale to promote the sale of soft