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Clustering
Creating mining models
Table 1 SSAS 2008 data mining algorithms and usage (continued) Usage
Algorithm Decision Trees
Decision Trees is the most popular DM algorithm, used to predict discrete and continuous variables. The algorithm uses the discrete input variables to split the tree into nodes in such a way that each node is more pure in terms of target variable each split leads to nodes where a single state of a target variable is represented better than other states. For continuous predictable variables, you get a piecewise multiple linear regression formula with a separate formula in each node of a tree. A tree that predicts continuous variables is a Regression Tree. Linear Regression predicts continuous variables, using a single multiple linear regression formula. The input variables must be continuous as well. Linear Regression is a simple case of a Regression Tree, a tree with no splits. As Linear Regression is a simple Regression Tree, a Logistic Regression is a Neural Network without any hidden layers. The Na ve Bayes algorithm calculates probabilities for each possible state of the input attribute for every single state of predictable variable. These probabilities are used to predict the target attribute based on the known input attributes of new cases. The Na ve Bayes algorithm is quite simple; it builds the models quickly. Therefore, it s suitable as a starting point in your prediction project. The Na ve Bayes algorithm doesn t support continuous attributes. The Neural Network algorithm is often associated with artificial intelligence. You can use this algorithm for predictions as well. Neural networks search for nonlinear functional dependencies by performing nonlinear transformations on the data in layers, from the input layer through hidden layers to the output layer. Because of the multiple nonlinear transformations, neural networks are harder to interpret compared to Decision Trees. Sequence Clustering searches for clusters based on a model, and not on similarity of cases as Clustering does. The models are defined on sequences of events by using Markov chains. Typical usage of Sequence Clustering would be an analysis of your company s website usage, although you can use this algorithm on any sequential data. You can use the Time Series algorithm to forecast continuous variables. Internally, the Time Series uses two different algorithms. For shortterm forecasting, the AutoRegression Trees (ART) algorithm is used. For longterm prediction, the AutoRegressive Integrated Moving Average (ARIMA) algorithm is used. You can mix the blend of algorithms used by using the mining model parameters.

