I'm listing the various data mining algorithms.
These are as follows:
Classification algorithms : A classification algorithm predicts one or more discrete variables, based on the other attributes in the dataset.
A regression algorithm predicts one or more continuous variable such as profit or loss, based on other attributes in the dataset.
A segmentation algorithm divides data into groups or clusters or items that have similar properties.
Association algorithms finds correlations between different attributes in a dataset. This is used for creating association rules.
Sequence analysis algorithms summarize frequent sequences or episodes in data
An algorithm can create a mining model that comprises of
a set of clusters that describe groupings within the data set
a decision tree that predicts an outcome
a mathematical model that forecasts sales
a set of rules that describe how items are grouped together.
Algorithms can be picked based on the purpose at hand.
Decision tree algorithm can be used to predict a discrete or continuous attribute. It can also be used to find groups of common items in transactions.
A Naive Bayes algorithm works best to predict a discrete attribute.  A neural network algorithm could be used too. A clustering algorithm also works well to predict a discrete attribute. However, it is better suited for grouping of similar items. A sequence clustering algorithm can be used to find groups of similar items as well as to predict a sequence.
A time series algorithm and a linear regression algorithm works best to determine a continuous attribute.
Association algorithm works well to find groups of common items by establishing correlations between attributes.