This week lecture talked about the advanced data mining techniques - Regression and Neural Network.
There are 3 types of regression models.
There are 3 types of regression models.
- Linear regression
- Nonlinear regression
- Logistic regression
Regression model are normally being used for:
- Fit data
- Time-series data: Forecast
- Other data: Predict
I do not really understand what is the meaning of fit data, hence I did some googling. And it seems that fit data means putting a straight line into a non-linear graph.
Typically, a best straight line will be drawn.According to Wikipedia, it says that regression analysis helps us understand how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed.
The following week lab lesson teaches us about how we could use Microsoft Excel to do an regression analysis by using LINEST function.
Besides using regression model for prediction, Neural Network provides both supervised and unsupervised modeling. It is quite similar to regression model except that the algorithms are different.
From the lecture slide, Neural Network is a computer technology that attempts to build computers that will operate like a human brain. The machines possess simultaneous memory storage and works with ambiguous information.
Neural Network was frequently used for approval of loan application and fraud prevention. It also includes time-series forecasting like regression.
There are two types of Neural Network:
There are two types of Neural Network:
- Feed-Forward Neural Network - Supervised Learning
- Kohonen Neural Network - Unsupervised Learning
So far, we only have done before feed-forward neural network in our data mining project. However, the interpretation of Neural Network is still quite difficult. And this is one of the major cons as it lacks of explanation.
Kohonen Neural Network is not being used before because all attributes got to be numeric, hence categorical attributes will need to be converted into numeric first. Again, it also lacks of explanation capability.
Regression and Neural Network are both interesting data mining techniques. But it also requires some skills for miners to convert the data to the required format for the data mining tools to mine.
Kohonen Neural Network is not being used before because all attributes got to be numeric, hence categorical attributes will need to be converted into numeric first. Again, it also lacks of explanation capability.
Regression and Neural Network are both interesting data mining techniques. But it also requires some skills for miners to convert the data to the required format for the data mining tools to mine.
