Simple Explanation Of Mining Algorithms

Hello Everyone,



I am reading DataMining Tutorial and right now I am at the Mining Algorithms section. I cannot understand any of the algorithms. For example take the following text... what a bunch of mouthful bla bla bla it is ....


"The Microsoft Decision Trees algorithm supports both classification and regression and it works well for predictive modeling. Using the algorithm, you can predict both discrete and continuous attributes.
In building a model, the algorithm examines how each input attribute in the dataset affects the result of the predicted attribute, and then it uses the input attributes with the strongest relationship to create a series of splits, called nodes. As new nodes are added to the model, a tree structure begins to form. The top node of the tree describes the breakdown of the predicted attribute over the overall population. Each additional node is created based on the distribution of states of the predicted attribute as compared to the input attributes. If an input attribute is seen to cause the predicted attribute to favor one state over another, a new node is added to the model. The model continues to grow until none of the remaining attributes create a split that provides an improved prediction over the existing node. The model seeks to find a combination of attributes and their states that creates a disproportionate distribution of states in the predicted attribute, therefore allowing you to predict the outcome of the predicted attribute"



In the above text what is meant by discrete and continious attributes? what is regression? what is predicted attributes? what are input attributes? what is distribution of states?



Is there a source which explains these algorighms in a easier way ....