DATA MINING

INTRODUCTION:

Data mining is the process of extracting patterns from data. Data mining is an important tool to transform data into information. It is commonly used in a wide range of profiling practices, such as marketing, surveillance, fraud detection and scientific discovery.

TASKS INVOLVED IN DATA MINING:

Data mining commonly involves following tasks:

1. Classification Classification is the procedure in which individual items are placed into groups based on quantitative information on one or more characteristics inherent in the items. Common algorithms includes decision tree learning, nearest neighbor, naive Bayesian classification and neural networks.

2. Clustering: Clustering is the assignment of a set of observations into subsets (called clusters) so that observations in the same cluster are similar in some sense. Clustering is a a common technique for statistical data analysis used in many fields, including machine learning, data mining, pattern recognition and bioinformatics image analysis.

Clustering are of following types:
(a). Hierarchical clustering.
(b). Partitional clustering.
(i). k-means clustering.
(ii). Fuzzy c-means clustering.
(c). Spectral clustering.

3. Regression: In statistics, regression analysis includes techniques for modeling and analyzing several variables, focus is on the relationship between a dependent variable and one or more independent variables.
Regression analysis helps us to understand how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held constant.

4. Association rule learning: Association rule learning is a popular and well researched method for discovering interesting relations between variables in large databases.
For example a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is referred to as market basket analysis.

In Supermarket case, if a customer buys milk and bread then a customer may also buy butter. {Milk,Bread}=>{Butter}.

BUSINESS INTELLIGENCE

INTRODUCTION:

Business Intelligence (BI) refers to computer-based techniques which are used in spotting, digging-out, and analyzing business data, such as sales revenue by products or departments or associated costs and incomes.

FUNCTIONS OF BI:

Common functions of Business Intelligence technologies are:
1. Reporting.
2. Online analytical processing.
3. Analytics.
4. Data mining.
5. Business performance management.
6. Benchmarking.
7. Text mining.
8. Predictive analytics.

HISTORY OF BI:

In a 1958 article, IBM researcher Hans Peter Luhn used the term business intelligence. He defined intelligence as: "the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal."

BUSINESS INTELLIGENCE AND DATA WAREHOUSING:

BI applications oftenly uses data gathered from a data warehouse or a data mart. However, not all data warehouses are used for business intelligence, nor do all business intelligence applications require a data warehouse.