Download Huge amount of data is available as the technology is advancing

Huge amount of data is available as the technology is advancing. Frequent itemset
mining discloses the inherent patterns and their properties in the data. Many
motivating scenarios include:
In a market basket analysis identifying the items which are often purchased
together

In a DNA sequence analysis what kind of DNA is sensitive to the new drug

In the Web log analysis, are we able to identify and cluster the web
documents properly.
The aim of frequent itemset mining is to discover those sets of items in the dataset
which appear frequently. For example consider the general example of the shopping
history database retrieved from a computer hardware vendor. A subsequence such as
buying a PC, then a digital camera and then a memory card if it occurs frequently in
the database is called a frequent pattern. There are basically two kinds of frequent
patterns which are a)frequent itemsets and b)frequent sequences. The basic difference
between both of them is that the former deals with an unordered collection of items
whereas the latter deals with ordered items.
Frequent patterns have a wide range of scope in many areas like Marketing, Sports, ecommerce etc. They are also used in other data mining tasks such as Classification and
Clustering. Their broad application scope makes them a very rewarding task in data
mining. They also serve as condensed representations of the records in the dataset. In
this unit, we will cover the terminology associated with the frequent patterns as well
as the existing popular algorithms for mining them.