mining functionalities are used to specify what kind of pattern are present in
our data during data mining tasks.
We can further divide data mining tasks into
two different categories.
1. Descriptive mining task
2. Predictive mining task
Descriptive mining task
descriptive mining tasks we try to find out the general properties present in
our data. For example, we find data describing patterns and come up with new
and significant information present in our available dataset.
Predictive mining task
predictive mining tasks we try to find out some inference on the current data
in order to make some predictions from the available data for the future.
What is Characterization and Discrimination?
we try to summarize some general characteristics or features present in our
target class of data then it's known as Data Characterization. Whereas
when we try to compare general features of target class data objects with the
general features of objects form one or a set of contrasting classes then its
known as Data Discrimination.
What we try to find during data mining?
performing data mining we try to find out what frequent pattern is present in
the data, is there any associations is present in our data and does our data
have some correlation present in them.
we try to find out what are some frequent patterns present in our data. There
are many kinds of frequent patterns present. Some of the present patterns are
subsequences, item sets, and substructures.
analysis is a type of analysis where we try to find out is there any
association present in our data.
imagine, you are a store manager of a big mart, you want to find out that which
items are frequently purchased together within the same transactions.
example, we may know, most of the time bread and butter are purchased together,
egg and bread are bought together.
(X, “bread”) = Purchased (X, “butter”) with support = 1% and confidence = 50%.
X represents a variable customer. Confidence = 505 means that is a customer
purchase a bread, there is a 50% change that he/she will also buy butter.
= 1% means that from all the transactions taken under analysis bread and butter
were purchased together in 1% of all the transactions.
Classification and Prediction
is a process where we try to find a model that can describe and distinguishes
data into different classes and then the model can be use for the prediction of
the class of objects whose class label is unknown.
What is Cluster Analysis?
we try to divide our data into different clusters based on some similarity
between the data points where we don’t have a target variable then it is known asCluster Analysis or
the above figure, we can see that the data points are divided into three
different clusters based on some similarities between the data points.
hope after reading this article, finally, you came to know about what are
some of the Verified data mining functionalities?