INTRODUCTION TO KNOWLEDGE DISCOVERY AND DATA MINING - CHAPTER 6

Data Mining with Neural Networks - Mạng lưới thần kinh nhân tạo được phổ biến bởi vì họ có một hồ sơ theo dõi đã được chứng minh trong nhiều ứng dụng dữ liệu khai thác mỏ và hỗ trợ quyết định. Họ đã được áp dụng trên một loạt các ngành công nghiệp, từ xác định hàng loạt tài chính để chẩn đoán các điều kiện y tế, từ việc xác định các cụm của khách hàng có giá trị để xác định các giao dịch thẻ tín dụng lừa đảo, công nhận con số bằng văn. | Chapter 6 Data Mining with Neural Networks Artificial neural networks are popular because they have a proven track record in many data mining and decision-support applications. They have been applied across a broad range of industries from identifying financial series to diagnosing medical conditions from identifying clusters of valuable customers to identifying fraudulent credit card transactions from recognizing numbers written on checks to predicting the failure rates of engines. Whereas people are good at generalizing from experience computers usually excel at following explicit instructions over and over. The appeal of neural networks is that they bridge this gap by modeling on a digital computer the neural connections in human brains. When used in well-defined domains their ability to generalize and learn from data mimics our own ability to learn from experience. This ability is useful for data mining and it also makes neural networks an exciting area for research promising new and better results in the future. Neural Networks for Data Mining A neural processing element receives inputs from other connected processing elements. These input signals or values pass through weighted connections which either amplify or diminish the signals. Inside the neural processing element all of these input signals are summed together to give the total input to the unit. This total input value is then passed through a mathematical function to produce an output or decision value ranging from 0 to 1. Notice that this is a real valued analog output not a digital 0 1 output. If the input signal matches the connection weights exactly then the output is close to 1. If the input signal totally mismatches the connection weights then the output is close to 0. Varying degrees of similarity are represented by the intermediate values. Now of course we can force the neural processing element to make a binary 1 0 decision but by using analog values ranging between and as the .