Credit scoring in Data Mining:The process of determine grant the customer a loan or not is very challenging and time consuming so banks and lending institute tend recently to use data mining techniques in deciding 2.Knowledge Data Discovery Process:Knowledge Data Discovery (KDD) process, which it is transform raw data to knowledge. Generally KDD and data mining used as the same thing. KDD consist from the following steps:Data cleaning: remove noise and irrelevant data.Data integration: combine multiple data sources.Data selection: retrieve relevant data to the analysis.Data transformation: transform data into form appropriate for mining.Data mining: apply techniques to extract patterns.Pattern evaluation: detect interesting patterns.Knowledge representation: visualization and representation techniques are used to present mined knowledge to users.The next figure show the KDD steps:Figure 2.1Data Mining:Data mining is the process of identifying patterns in data, patterns that is valid, useful and understandable 3. Data mining consider as the main step in KDD. It involve the following common classes of task:Anomaly detection:”Anomaly detection refers to the problem of finding patterns in data that do not conform to expected behavior.” 5 These nonconforming patterns are often referred to as anomalies or outliers. It involves algorithm that can discover real anomalies. It consider any object don’t follow the normal behavior as anomalies. Association analysis:Is finding objects that arise frequently together 6, such as symptoms of illness appear together. Association analysis technique as piori algorithmClassification: Is the process of discover a set of models that describe and distinguish data classes and concepts, for the purpose of being able to use the model to predict the class whose label is unknown 4. Its techniques such as decision tree and neural network.Regression:Regression is used to predict missing or unavailable numerical data values rather than (discrete) class labels. The term prediction refers to both numeric prediction and class label prediction 4.Summarization.Clustering:It partition the objects into clusters, so that objects within a cluster are like to one another and unlike to objects in other clusters 4. Such as K-means algorithm. 


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