Ghada H. El-Khawaga Marwa M. El-Sadeeq 2007

 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

 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.

 Data volumes are too large for classical analysis approaches: Large number of records High dimensional data  Leverage organization’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

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

 Statistical methods  Case-based reasoning  Neural networks  Decision trees

Data warehousing + data mining = increased performance of decision making process + knowledgeable decision makers  SQL Vs. Data mining Vs. OLAP

 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

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

 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.

 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

 Data Mining: Concepts, Models, Methods, and Algorithms, Mehmed Kantardzic, ISBN: , John Wiley & Sons ©  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