Validating cluster structures in data mining tasks sydney lovers sydney dating service
There are many kinds of frequent patterns, including itemsets, subsequences, and substructures.
Suppose, as a marketing manager, you would like to determine which items are frequently purchased together within the same transactions. Confidence=50% means that if a customer buys a computer, there is a 50% chance that she will buy software as well.
That is, clusters of objects are formed so that objects within a cluster have high similarity in comparison to one another, but are very dissimilar to objects in other clusters.
A database may contain data objects that do not comply with the general behavior or model of the data. Most data mining methods discard outliers as noise or exceptions.
A decision tree is a flow-chart-like tree structure, where each node denotes a test on an attribute value, each branch represents an outcome of the test, and tree leaves represent classes or class distributions.
The objects are grouped based on the principle of maximizing the intraclass similarity and minimizing the interclass similarity.
It keep on doing so until all of the groups are merged into one or until the termination condition holds.
This approach is also known as the top-down approach.
This method is rigid, i.e., once a merging or splitting is done, it can never be undone.
Data discrimination is a comparison of the general features of target class data objects with the general features of objects from one or a set of contrasting classes.
Frequent patterns, are patterns that occur frequently in data.
In this, we start with each object forming a separate group.
It keeps on merging the objects or groups that are close to one another.