Data Mining - Introduction
- Attribute Types
- Basic Statistical Descriptions of Data
- Knowledge Discovery from Data (KDD)
- Data Mining architecture
- Types of Data
- Data Mining Functionalities
- Interestingness Patterns
- Classification of Data Mining systems
- Data mining Task primitives
- Integration of Data mining system with a Data warehouse
- Major issues in Data Mining
- Data Preprocessing
- Association Rule Mining
- Mining Frequent Patterns
- Associations and correlations
- Mining Methods
- Apriori algorithm
- FP-Growth algorithm
- Mining Various kinds of Association Rules
- Correlation Analysis
- Constraint based Association mining
- Graph Pattern Mining
- SPM(Sequential Pattern Mining)
- Classification
- Basic concepts
- Decision tree induction
- Bayesian classification
- Rule–based classification
- Lazy learner(K-NN)
Mining Frequent Patterns
Mining Frequent Patterns in Data Mining
Item Set:
An Item set is collection or set of items
Examples:
{Computer, Printer, MSOffice} is 3 item set
{Milk, Bread} is 2 item set
Similarly,
Set of K items is called k item set
Frequent patterns
These are patterns that appear frequently in a data set. Patterns may be item sets, or sub sequences.
Example: Transaction Database (Dataset)
| TID | Items |
|---|---|
| T1 | Bread, Coke, Milk |
| T2 | Popcorn, Bread |
| T3 | Bread, Egg, Milk. |
| T4 | Egg, Bread, Coke, Milk |
A set of items, such as Milk & Bread that appear together in a transaction data set (Also called as Frequent Item set).
Frequent item set mining leads to the discovery of associations and correlations among items in large transactional (or) relational data sets.
Finding frequent patterns plays an essential role in mining associations, correlations, and many other interesting relationships among data. Moreover, it helps in data classification, clustering, and other data mining tasks.
Next Topic :Associations and correlations