Data Mining Technniques
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The document discusses data mining and data warehousing, explaining that a data warehouse integrates data from multiple sources to enhance decision-making. It outlines various data mining techniques, including classification analysis, clustering analysis, association rule learning, anomaly detection, and regression analysis, each of which serves specific analytical purposes. Furthermore, it highlights real-life applications of data mining in sectors such as retail, e-commerce, and crime analysis.
Data Mining Technniques
- 1. DATA MINING TECHNIQUES DATA WAREHOUSING TOP DATA MINING TECHNIQUES
- 2. Data Warehousing • A data warehouse is a collection of databases that work together. A data warehouse makes it possible to integrate data from multiple databases, which can give new insights into the data. • The ultimate goal of a database is not just to store data, but to help businesses make decisions based on that data.
- 3. What is data mining? • Queries based on SQL, a database programming language, are used to answer basic questions about data. • But, as the collection of data grows in a database, the amount of data can easily become overwhelming. • Data mining is the process of analyzing data and summarizing it to produce useful information
- 4. Data sets • The complete set of data available to us for an application is called a dataset. A dataset is often depicted as a table, with each row representing an instance. Each column contains the value of one of the variables (attributes) for each of the instances.
- 5. Goal of Data Mining • The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. • Data mining is the computing process of discovering patterns in large data sets involving methods. • These patterns can be used for predictive analytics.
- 6. Techniques used for mining • CLASSIFICATION ANALYSIS • ASSOCIATION RULE LEARNING • ANOMALY OR OUTLIER DETECTION • CLUSTERING ANALYSIS • REGRESSION ANALYSIS
- 7. CLUSTERING ANALYSIS • Clustering analysis is the process of discovering groups and clusters in the data in such a way that the degree of association between two objects is highest if they belong to the same group and lowest otherwise. • The cluster is actually a collection of data objects.
- 8. ASSOCIATION RULE LEARNING • It refers to the method that can help you identify some interesting relations between different variables in large databases. • Association rules are useful for examining and forecasting customer behavior. It is highly recommended in the retail industry analysis.
- 9. ANOMALY OR OUTLIER DETECTION • This refers to the observation for data items in a dataset that do not match an expected pattern or an expected behavior. • This technique can be used in a variety of domains, such as intrusion detection.
- 10. CLASSIFICATION ANALYSIS • This analysis is used to retrieve important and relevant information about data. It is used to classify different data in different classes. • A classic example of classification analysis would be our Outlook email. In Outlook, they use certain algorithms to characterize an email as legitimate or spam.
- 11. REGRESSION ANALYSIS • In statistical terms, a regression analysis is the process of identifying and analyzing the relationship among variables. • It can help you understand the characteristic value of the dependent variable changes, if any one of the independent variables is varied. • It is generally used for prediction and forecasting.
- 12. Real life applications • Service providers • Retail • E-commerce • Crime agencies
- 13. Thank You