data mining full report

[project topics]

Data mining introduction
Ghada H. El-Khawaga
Marwa M. El-Sadeeq
2007
Agenda
What is data mining ?
Why data mining?
Data mining types
Data mining tasks
Knowledge discovery in databases (KDD) processes
Data mining processes
Data mining techniques
Data mining and Data warehousing
Data Mining System Components
Data Mining Applications
Data Mining Tools






What is data mining ?
Non-trivial extraction of implicit, previously unknown and potentially useful information from data.

A step in the knowledge discovery process consisting of particular algorithms (methods) that under some acceptable objective, produces a particular enumeration of patterns (models) over the data.

Why data mining ?
Data volumes are too large for classical analysis approaches:
Large number of records
High dimensional data
Leverage organizationâ„¢s data assets
Only a small portion of the collected data is ever analyzed
Data that may never be analyzed continues to be collected, at a great expense, out of fear that something which may prove important in the future is missing.
As databases grow, the ability to support the decision support process using traditional query languages becomes infeasible
Query formulation problem








Data mining types
Predictive data mining: which produces the model of the system described by the given data. It uses some variables or fields in the data set to predict unknown or future values of other variables of interest.

Descriptive data mining: which produces new, nontrivial information based on the available data set. It focuses on finding patterns describing the data that can be interpreted by humans.

Data mining tasks
Data processing [descriptive]
Prediction [predictive]
Regression [predictive]
Clustering [descriptive]
Classification [predictive]
Link analysis/ associations [descriptive]
Evolution and deviation analysis [predictive]

Knowledge Discovery in Databases (KDD) processes
Data mining processes
Data mining techniques
Statistical methods
Case-based reasoning
Neural networks
Decision trees


Data mining and Data warehousing
Data warehousing + data mining =

increased performance of decision making process
+
knowledgeable decision makers

SQL Vs. Data mining Vs. OLAP

Data Mining Applications
Data Mining For Financial Data Analysis
Data Mining For Telecommunications Industry
Data Mining For The Retail Industry
Data Mining In Healthcare and Biomedical Research
Data Mining In Science and Engineering

Data Mining System Components
The Function of the data mining system is to
assign scores to various profiles.

Data Mart
Data Mining System(Processing)
Operational Data Store
Scoring Software
Reporting System

Data Mining Applications
Data Mining For Financial Data Analysis
In Banking Industry data mining is used :
1- in the predicting credit fraud
2- in evaluation risk
3- in performing trend analysis
4- in analyzing profitability
5- in helping with direct marketing campaigns

In financial markets and neural networks data mining is used :
1- forecasting stock prices
2- forecasting commodity-price prediction
3- forecasting financial disasters


Data Mining Applications
Data Mining For Telecommunications Industry
- Answering some strategic questions through data-mining applications such as:
1-How does one retain customers and keep them loyal
as competitors offer special offers and reduced rates?
2-When is a high-risk investment, such as new fiber optic
lines, acceptable?
3-How does one predict whether customers will buy
additional products like cellular services, call waiting,
or basic services?
4-What characteristics differentiate our products from those of
our competitors?










Data Mining Applications
Data Mining For The Retail Industry
-The retail industry is a major application area for data mining since it collects huge amounts of data on sales, customer-shopping history, goods transportation, consumption patterns, and service records.
-Retailers are interested in creating data-mining models to answer questions such as:
1- What are the best types of advertisements to reach certain segments of customers?
2- What is the optimal timing at which to send mailers?
3- What types of products can be sold together?
4- How does one retain profitable customers?
5- What are the significant customer segments that
buy products?




Data Mining Applications
Data Mining In Healthcare and Biomedical Research
- Storing patients' records in electronic format and the development in medical-information systems cause a large amount of clinical data to be available online. Regularities, and surprising events extracted from these data by data-mining methods are important in assisting clinicians to make informed decisions, thereby improving health services.
- data mining has been used in many successful medical applications, including data validation in intensive care, the monitoring of children's growth, analysis of diabetic patient's data, the monitoring of heart-transplant patients.


Data Mining Applications
Data Mining In Science and Engineering

- a few important cases of data-mine applications in engineering problems. Pavilion Technologies' Process Insights, an application-development tool that combines neural networks, fuzzy logic, and statistical methods was used to develop chemical manufacturing and control applications to reduce waste, improve product quality, and increase plant throughput.



Data Mining Tools
Data Mind
Agent Base/Marketer
DB Miner
Decision Series
IBM Intelligent Miner
Data Mining Suite
Darwin (now part of Oracle)
Business Miner
Data Engine




Data Mining Tools
Agent Base/Marketer
It is based on emerging intelligent-agent technology.
It can access data from all major sources, and it runs on Windows95, Windows NT, and the Solaris operating system.
Business Miner
It is a single-strategy, easy-to-use tool based on decision trees.
It can access data from multiple sources including Oracle, Sybase, SQL Server, and Teradata.
It runs on all Windows platforms
Data Engine
It is a multiple-strategy data-mining tool for data modeling, combining conventional data-analysis methods with fuzzy technology, neural networks, and advanced statistical techniques.
It works on the Windows platform.



Problems of Data Mining Tools
Difficult to use
Needs Expert to run the tool
Difficult to add new functionality
Difficult to interface
Short lifetime
Limited Number of algorithms
Need lot of resources
References
Data Mining: Concepts, Models, Methods, and Algorithms, Mehmed Kantardzic, ISBN:0471228524, John Wiley & Sons © 2003.
Privacy data mining report, DHS privacy office,2005. 
Building Data Mining Solutions with OLE DB for DM and XML for Analysis, Zhaohui Tang, Jamie Maclennan, Peter Pyungchul Kim, SIGMOD Record, Vol. 34, No. 2, June 2005