Data Mining is defined as extracting the information from the huge set of data. In other words we can say that data mining is mining the knowledge from data. This information can be used for any of the following applications:
Market Analysis
Fraud Detection
Customer Retention
Production Control
Science Exploration
Market Analysis and Management
Following are the various fields of market where data mining is used:
Customer Profiling - Data Mining helps to determine what kind of people buy what kind of products.
Identifying Customer Requirements - Data Mining helps in identifying the best products for different customers. It uses prediction to find the factors that may attract new customers.
Cross Market Analysis - Data Mining performs Association/correlations between product sales.
Target Marketing - Data Mining helps to find clusters of model customers who share the same characteristics such as interest, spending habits, income etc.
Determining Customer purchasing pattern - Data mining helps in determining customer purchasing pattern.
Providing Summary Information - Data Mining provide us various multidimensional summary reports
Corporate Analysis & Risk Management
Following are the various fields of Corporate Sector where data mining is used:
Finance Planning and Asset Evaluation - It involves cash flow analysis and prediction, contingent claim analysis to evaluate assets.
Resource Planning - Resource Planning It involves summarizing and comparing the resources and spending.
Competition - It involves monitoring competitors and market directions
Data Mining Complexity and Issues:
Data mining is not that easy. The algorithm used are very complex. The data is not available at one place it needs to be integrated form the various heterogeneous data sources. These factors also creates some issues. Here in this tutorial we will discuss the major issues regarding:
Mining Methodology and User Interaction
Performance Issues
Diverse Data Types Issues
The following diagram describes the major issues.
Query Language:
The Data Mining Query Language was proposed by Han, Fu, Wang, et al for the DBMiner data mining system. The Data Mining Query Language is actually based on Structured Query Language (SQL). Data Mining Query Languages can be designed to support ad hoc and interactive data mining. This DMQL provides commands for specifying primitives. The DMQL can work with databases data warehouses as well. Data Mining Query Language can be used to define data mining tasks. Particularly we examine how to define data warehouse and data marts in Data Mining Query Language.
Task-Relevant Data Specification Syntax
Here is the syntax of DMQL for specifying the task relevant data:
use database database_name,
or
use data warehouse data_warehouse_name
in relevance to att_or_dim_list
from relation(s)/cube(s) [where condition]
order by order_list
group by grouping_list
