DATA MINING AND DW FOR BCA - UNIT-1
Data Mining:
Data mining is defined as the procedure of extracting information from large sets of data i.e.
there is a large of data available in the industry. This data is of no use until it is converted into useful
information. It is necessary to analyze this large amount of data and extract useful information.
Sometimes referred as
Knowledge Extraction
Knowledge Mining
Pattern Anaysis
Data Archeology
Areas of Data mining:
Financial Data Analysis:
The financial data in banking and financial industry is generally reliable and of high
quality which facilities systematic data analysis and data mining. Some of the typical
cases are as follows:
Loan payment prediction and customer credit policy analysis.
Classification and clustering of customers for targeted marketing
Detection of money laundering and other financial crimes
Retail Industry:
Data mining in retail industry helps in identifying customer buying items and trends
that lead to improved quality of customer services and good customer retention and
satisfaction.
Telecommunication Industry:
Data mining in telecommunication industry helps in identifying the
telecommunication pattern, catch fraudulent activities, make better se of resources, and
improve quality of services.
Biological Data Analysis:
In recent times, we have seen a tremendous growth in the field of biology such as
genomics, proteomics, functional Genomics and biomedical researches. Biological data
mining is a very important part of Bioinformatics.
Mining Frequent Patterns and Associations
Frequent Itemset Mining :
Frequent Itemset Mining (FIM) is one of the most well known techniques to extract
knowledge from data. FIM is the technique used mostly in field of data mining like finance,
health care system.
Example 1: Most important use of FIM is customer segmentation in marketing, shopping cart
analyzes, management relationship, web usage mining, and player tracking and so on.
Example 2: FIM in Market Basket Analysis:
This process analyzes customer buying habits by finding associations between the different
items that customers place in their “shopping baskets” as shown in the adjacent figure.
The discovery of such associations can help retailers develop marketing strategies by
gaining insight into which items are frequently purchased together by customers.
For instance, if customers are buying milk, how likely are they to also buy bread (and
what kind of bread) on the same trip to the supermarket.
Such information can lead to increased sales by helping retailers do selective marketing
and plan their shelf space. The concept of Frequent Itemsets, Closed Itemsets and Association Rules:
Frequent Itemsets: A set of items is referred to as an itemset. An itemset that contains k items
is a k-itemset.
Example: The set {milk, bread, eggs, sugar} is a 4-itemset.
The occurrence frequency (called support count, frequency or count)of an itemset is the
number of transactions that contain the itemset.
If the relative support of an itemset I satisfies a prespecified minimum support threshold
(i.e., the absolute support of I satisfies the corresponding minimum support count
threshold), then I is a frequent itemset.
The set of frequent k-itemsets is commonly denoted by Lk.
Closed Itemsets: A closed itemset is set of items which is as large as it can possibly be without
losing any transactions.
It is a frequent itemset that is both closed and its support is greater than or equal to
minsup.
An itemset is closed in a data set if there exists no superset that has the same support
count as this original itemset.
Comments