Bharati M. Ramageri / Indian Journal of Computer Science and Engineering
Vol. 1 No. 4 301-305
2.3. Predication
Regression technique can be adapted for predication. Regression analysis can be used
to model the
relationship between one or more independent variables and dependent variables. In data mining
independent variables are attributes already known and response variables are what we want to predict.
Unfortunately, many real-world problems are not simply prediction. For instance,
sales volumes, stock
prices, and product failure rates are all very difficult to predict because they may depend on complex
interactions of multiple predictor variables. Therefore, more complex techniques (e.g.,
logistic regression,
decision trees, or neural nets) may be necessary to forecast future values. The same model types can often
be used for both regression and classification. For example, the CART (Classification and Regression
Trees) decision tree algorithm can be used to build both classification trees (to classify categorical
response
variables) and regression trees (to forecast continuous response variables). Neural networks too can create
both classification and regression models.
Types of regression methods
Linear Regression
Multivariate Linear Regression
Nonlinear Regression
Multivariate Nonlinear Regression
2.4. Association rule
Association and correlation is usually to find frequent item set findings among large data sets.
This type of
finding helps businesses to make certain decisions, such as catalogue design, cross marketing and customer
shopping behavior analysis. Association Rule algorithms need to be able to generate
rules with confidence
values less than one. However the number of possible Association Rules for a given dataset is generally
very large and a high proportion of the rules are usually of little (if any) value.
Types of association rule
Multilevel association rule
Multidimensional
association rule
Quantitative association rule
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