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Cơ sở dữ liệu được phong phú với các thông tin ẩn có thể được sử dụng cho việc ra quyết định thông minh. Phân loại và dự báo là hai hình thức phân tích dữ liệu có thể được sử dụng để trích xuất các mô hình mô tả các lớp dữ liệu quan trọng hoặc dự đoán các xu hướng dữ liệu trong tương lai. | Classification and Prediction Databases are rich with hidden information that can be used for intelligent decision making. Classification and prediction are two forms of data analysis that can be used to extract models describing important data classes or to predict future data trends. Such analysis can help provide us with a better understanding of the data at large. Whereas classification predicts categorical discrete unordered labels prediction models continuousvalued functions. For example we can build a classification model to categorize bank loan applications as either safe or risky or a prediction model to predict the expenditures in dollars of potential customers on computer equipment given their income and occupation. Many classification and prediction methods have been proposed by researchers in machine learning pattern recognition and statistics. Most algorithms are memory resident typically assuming a small data size. Recent data mining research has built on such work developing scalable classification and prediction techniques capable of handling large disk-resident data. In this chapter you will learn basic techniques for data classification such as how to build decision tree classifiers Bayesian classifiers Bayesian belief networks and rulebased classifiers. Backpropagation a neural network technique is also discussed in addition to a more recent approach to classification known as support vector machines. Classification based on association rule mining is explored. Other approaches to classification such as fc-nearest-neighbor classifiers case-based reasoning genetic algorithms rough sets and fuzzy logic techniques are introduced. Methods for prediction including linear regression nonlinear regression and other regression-based models are briefly discussed. Where applicable you will learn about extensions to these techniques for their application to classification and prediction in large databases. Classification and prediction have numerous .
