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Data Mining and Data Warehousing Introduction to Data Mining Motivation To get complete understanding about how important information is extracted from raw data and how that information helps people in making day to day choices. Introduction ppt (For your convenience you can get them inside Learn More Quadrant) Learning Objectives By the end of this module, the learner will be able to: Define data mining Compare various data mining tasks Identify various applications of data mining Discover the various aspects of data mining Justify the challenges in data mining Imagine various scenarios where data mining can be applied Prerequisite For this topic, the learner should have an understanding of basic database concepts such as schema, ER model and Structured Query language. It gives a basic idea about how mining of data is used in various algorithms as well as how it is related to our day to day activities. Suggested Time 150 mins Concept Data Mining Process of semi-automatically analyzing large databases to find patterns that are: Valid: holds new data with some certainty Novel: non-obvious to the system Useful: should be possible to act on the item Understandable: humans should be able to interpret the pattern Also known as Knowledge Discovery in Databases (KDD) Need for data mining Here are the reasons listed below: In the field of Information technology we have huge amount of data available that needs to be turned into useful information. This information further can be used for various applications such as market analysis, fraud detection, customer retention, production control, science exploration etc. Applications Banking (loan/credit card approval): predict the good customers based on old customers Customer relationship management: Identify those who are likely to leave for a competitor. Targeted marketing: Identify likely responders to promotions Fraud detection: Telecommunications, financial transactions-from an online stream of event, identify fraudulent events Manufacturing and production: Automatically adjust knobs when process parameter changes Medicine: Disease outcome, effectiveness of treatments. Analyze patient disease history. Find relationship between diseases Molecular/Pharmaceutical: Identify new drugs Scientific data analysis: Identify new galaxies by searching for sub clusters Web site/store design and promotion: Find affinity of visitor to pages and modify layout Major Data Mining Tasks Classification: Predicting an item class Association Rule Discovery: descriptive Clustering: descriptive, finding groups of items Sequential Pattern Discovery: descriptive Deviation Detection: predictive, finding changes Forecasting: predicting a parameter value Description: describing a group Link analysis: finding relationships and associations Bio Data Mining: DNA analysis Data Mining in use currently The Government uses Data Mining to track fraud A Supermarket becomes an information broker Basketball teams use it to track game strategy Cross Selling Target Marketing Holding on to Good Customers Weeding out Bad Customers Major concerns in Data Mining Mining various and new kinds of knowledge To make data mining user interactive, that includes, incorporation of background knowledge. Make data mining results visual and directly usable by humans. Make mining algorithm efficient and scalable. Use new technologies like cloud computing and cluster computing to make algorithms parallel and distributed. Preserve privacy while mining social data. Efficiently mining shared and networked repositories. Challenges in Data Mining Intelligent data analysis in Bio-Informatics Mining with data streams (in continuous, real-time, dynamic data environments) Mining complex knowledge from complex data Data Mining for Biological and Environmental problems Security, privacy and data integrity Illustration The process of mining Data using the Data Mining techniques will be as shown in the figure.