DATA MINING & TECHNIQUES

DATA MINING & TECHNIQUES

• 1. Slide19 DATA MINING & TECHNIQUES
• 2. Introduction Introduction There is a huge amount of data available in the Information Industry. This data is of no use until it is converted into useful information. It is necessary to analyze this huge amount of data and extract useful information from it. Extraction of information is not only a single process, data mining also involves other processes such as Data Cleaning, Data Integration, Data Transformation, Data Mining, Pattern Evaluation, and Data Presentation. Once all these processes are over, we would be able to use this information in many applications such as Fraud detection, Market analysis, Science exploration, etc
• 3. Slide3 What is Data Mining? Why Data Mining? What is KDD Process? On What Kind of Data? Data Mining Techniques Data Mining Query Language Applications of Data Mining
• 4. What is Data Mining? Extraction of interesting Patterns or Knowledge from huge amount of data (Knowledge Discovery from Data) One of the Step from KDD process What is Data Mining?
• 5. Why Data Mining? Why Data Mining? qThe Explosive Growth of Data: from terabytes to petabytes qWe are drowning in data, but starving for knowledge! qFraud detection and detection of unusual patterns
• 6. What is KDD process? What is KDD process? Data cleaning to remove noise and inconsistent data Data integration where multiple data sources may be combined Data selection Related Data Data transformation Unified format
• 7. Slide7 Data mining Extract Patterns Pattern evaluation to identify the truly interesting patterns representing knowledge Knowledge presentation Present the mined knowledge to the user
• 8. KDD PROCESS KDD PROCESS
• 9. On What kind of Data? On What kind of Data? Relational Databases Collection of tables Data Warehouses Data from different sources Transactional Databases Consists of a file where each record represent transactions Advanced Data & Applications Multimedia, Spatial data and WWW
• 10. Data Mining Techniques Data Mining Techniques Classification Clustering Regression Association Rules
• 11. Classification Classification Classification is the process of predicting the class of a new item. Therefore to classify the new item and identify to which class it belongs
• 12. Clustering Clustering Group Data into Clusters Similar data is grouped in the same cluster Dissimilar data is grouped in the same cluster
• 13. Regression Regression “Regression deals with the prediction of a value, rather than a class.” Regression is a data mining function that predicts a number For example, a regression model could be used to predict children's height, given their age, weight, and other factors.
• 14. Association Rules Association Rules “An association algorithm creates rules that describe how often events have occurred together.” Example: When a customer buys a Computer, then 90% of the time they will buy softwares.
• 15. Data Mining Query Language Data Mining Query Language A DMQL can provide the ability to support interactive data mining. Adopts SQL-like syntax Hence, can be easily integrated with relational query languages
• 16. Applications of Data Mining Applications of Data Mining Market Basket Analysis Market basket analysis is a modeling technique based upon a theory that if you buy a certain group of items you are more likely to buy another group of items. This information may help the retailer to know the buyer’s needs and retailer can enhance the store’s layout
• 17. Applications of Data Mining Applications of Data Mining Bio Informatics Mining biological data helps to extract useful knowledge from massive datasets gathered in biology, and in other related life sciences areas Applications of data mining to bioinformatics include gene finding, protein function inference, disease diagnosis, disease treatment
• 18. Applications of Data Mining Education Data mining can be used by an institution to take accurate decisions and also to predict the results of the student. Learning pattern of the students can be captured and used to develop techniques to teach them. Customer Relationships Management (CRM) To maintain a proper relationship with a customer a business need to collect data and analyze the information. With data mining technologies the collected data can be used for analysis. Applications of Data Mining
• 19. Thank You… Thank You…